An unsupervised method for physical cell interaction profiling of complex tissues
Authors: Agneta B Andersson
Nathanael Andrews
Jason T Serviss
Iva Šutevski
Ewa Dzwonkowska
Marco Gerling
Natalie Geyer
Ninib Baryawno
Rosan Heijboer
Martin Enge
Journal: Nature Methods (2021)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: Cellular identity; cell interaction; CIM-seq; physical organization; multicellular organisms
Affiliations: Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden., Department of Biosciences and Nutrition (BioNut), Karolinska Institutet, Huddinge, Sweden., Department of Children's and Women's Health, Karolinska Institutet, Stockholm, Sweden., Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden. .

Abstract:  Cellular identity in complex multicellular organisms is determined in part by the physical organization of cells. However, large-scale investigation of the cellular interactome remains technically challenging. Here we develop cell interaction by multiplet sequencing (CIM-seq), an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between cell types present in a tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution into constituent cell types. CIM-seq estimates parameters such as number of cells and cell types in each multiplet directly from sequencing data, making it compatible with high-throughput droplet-based methods. When applied to gut epithelium or whole dissociated lung and spleen, CIM-seq correctly identifies known interactions, including those between different cell lineages and immune cells. In the colon, CIM-seq identifies a previously unrecognized goblet cell subtype expressing the wound-healing marker Plet1, which is directly adjacent to colonic stem cells. Our results demonstrate that CIM-seq is broadly applicable to unsupervised profiling of cell-type interactions in different tissue types. 

Retracted: No

PubPeer comments:
 Hi, Thank you very much for putting together this publication. This is more of a question than a concern. However, I am posting this on PubPeer because I unfortunately don t believe Nature Methods currently has a public comment system (unlike other Nature journals, and others). You noted that  *our results from small intestinal epithelium showed that plate-based and droplet-based CIM-seq provided comparable results* . So, I think that is encouraging. However, for at least some 10x protocols, the technical rate of producing doublets varies as a function of the number of cells sorted: https: www.biostars.org p 9467937 Is this something that you have encountered? **For example, have you noticed certain total cell counts result in more reproduceable results with independent methods?** In the methods, you describe using *DoubletFinder*, but I was not sure if you had some sense of how well that captured true versus artificial multiplets (both in terms of having biologically meaningful false positives removed as well as having false negatives that remain for your analysis). Thank you very much. Sincerely, Charles , 
 Dear Charles, Reading your message, I think you might have misunderstood a central part of our method. CIM-seq does not use one  normal single cell suspension and attempt to find singlets doublets in it. Instead, CIM-seq uses FACS sorting to distinguish between singlets and multiplets before analysis. For a droplet experiment, this means that we will have two separate samples for each tissue experiment - one where cells are all singlets and one where they are mostly multiplets (typically ~70% multiplets, 30% singlets). As you mention, a typical 10x experiment will also contain doublets due to random incorporation of 1 cells in a droplet, which is related to the concentration of cells in the sample (typically ~1% of droplets will be duplets if you load conservatively). In the paper, we use DoubletFinder as a part of the singlet sample QC - in other words we use it to remove random doublets before building our cell type map, and not to identify multiplets for further CIM-seq analysis. In the last stage of CIM-seq analysis, we use the expected frequencies of cell type combinations to determine statistical overrepresentation of specific cell combinations - this is a bit involved but also detailed in the paper. I hope the explanation above cleared this up to you. As I said, this is a fundamental part of the method and is described in our paper. The FACS-based sorting into singlets multiplets is also similar to how previous methods have tackled the problem. If you need a more thorough explanation I suggest reading the  methods section carefully again, and for future clarifications I would encourage you to email the corresponding author directly. Best, Martin , 
 #2 Hi Martin, Thank you very much for your response. Yes - this is very helpful. I apologize about the misunderstanding, and I see the specific example relates to 2 *CIM-seq* experiments yielding similar results. I hope this might help if others have similar confusion, but I apologize that this may not be the most effective way to do that (and I don t think I can edit the beginning to direct others to important points, while still acknowledging my misunderstanding). Thanks Again, Charles ,