scholarly journals Transcriptional signatures of cell-cell interactions are dependent on cellular context

2021 ◽  
Author(s):  
Brendan T Innes ◽  
Gary D Bader

Cell-cell interactions are often predicted from single-cell transcriptomics data based on observing receptor and corresponding ligand transcripts in cells. These predictions could theoretically be improved by inspecting the transcriptome of the receptor cell for evidence of gene expression changes in response to the ligand. It is commonly expected that a given receptor, in response to ligand activation, will have a characteristic downstream gene expression signature. However, this assumption has not been well tested. We used ligand perturbation data from both the high-throughput Connectivity Map resource and published transcriptomic assays of cell lines and purified cell populations to determine whether ligand signals have unique and generalizable transcriptional signatures across biological conditions. Most of the receptors we analyzed did not have such characteristic gene expression signatures - instead these signatures were highly dependent on cell type. Cell context is thus important when considering transcriptomic evidence of ligand signaling, which makes it challenging to build generalizable ligand-receptor interaction signatures to improve cell-cell interaction predictions.

2018 ◽  
Vol 115 (48) ◽  
pp. 12112-12117 ◽  
Author(s):  
Rebekka E. Breier ◽  
Cristian C. Lalescu ◽  
Devin Waas ◽  
Michael Wilczek ◽  
Marco G. Mazza

Phytoplankton often encounter turbulence in their habitat. As most toxic phytoplankton species are motile, resolving the interplay of motility and turbulence has fundamental repercussions on our understanding of their own ecology and of the entire ecosystems they inhabit. The spatial distribution of motile phytoplankton cells exhibits patchiness at distances of decimeter to millimeter scales for numerous species with different motility strategies. The explanation of this general phenomenon remains challenging. Furthermore, hydrodynamic cell–cell interactions, which grow more relevant as the density in the patches increases, have been so far ignored. Here, we combine particle simulations and continuum theory to study the emergence of patchiness in motile microorganisms in three dimensions. By addressing the combined effects of motility, cell–cell interaction, and turbulent flow conditions, we uncover a general mechanism: The coupling of cell–cell interactions to the turbulent dynamics favors the formation of dense patches. Identification of the important length and time scales, independent from the motility mode, allows us to elucidate a general physical mechanism underpinning the emergence of patchiness. Our results shed light on the dynamical characteristics necessary for the formation of patchiness and complement current efforts to unravel planktonic ecological interactions.


2020 ◽  
Vol 11 (12) ◽  
pp. 866-880 ◽  
Author(s):  
Xin Shao ◽  
Xiaoyan Lu ◽  
Jie Liao ◽  
Huajun Chen ◽  
Xiaohui Fan

AbstractFor multicellular organisms, cell-cell communication is essential to numerous biological processes. Drawing upon the latest development of single-cell RNA-sequencing (scRNA-seq), high-resolution transcriptomic data have deepened our understanding of cellular phenotype heterogeneity and composition of complex tissues, which enables systematic cell-cell communication studies at a single-cell level. We first summarize a common workflow of cell-cell communication study using scRNA-seq data, which often includes data preparation, construction of communication networks, and result validation. Two common strategies taken to uncover cell-cell communications are reviewed, e.g., physically vicinal structure-based and ligand-receptor interaction-based one. To conclude, challenges and current applications of cell-cell communication studies at a single-cell resolution are discussed in details and future perspectives are proposed.


Author(s):  
Erick Armingol ◽  
Adam Officer ◽  
Olivier Harismendy ◽  
Nathan E. Lewis

Development ◽  
2001 ◽  
Vol 128 (7) ◽  
pp. 1211-1219 ◽  
Author(s):  
A. Arai ◽  
A. Nakamoto ◽  
T. Shimizu

In embryos of clitellate annelids (i.e. oligochaetes and leeches), four ectodermal teloblasts (ectoteloblasts N, O, P and Q) are generated on either side through a stereotyped sequence of cell divisions of a proteloblast, NOPQ. The four ectoteloblasts assume distinct fates and produce bandlets of smaller progeny cells, which join together to form an ectodermal germ band. The pattern of the germ band, with respect to the ventrodorsal order of the bandlets, has been highly preserved in clitellate annelids. We show that specification of ectoteloblast lineages in the oligochaete annelid Tubifex involves cell interaction networks distinct from those in leeches. Cell ablation experiments have shown that fates of teloblasts N, P and Q in Tubifex embryos are determined rigidly as early as their birth. In contrast, the O teloblast and its progeny are initially pluripotent and their fate becomes restricted to the O fate through an inductive signal emanating from the P lineage. In the absence of this signal, the O lineage assumes the P fate. These results differ significantly from those obtained in embryos of the leech Helobdella, suggesting the diversity of patterning mechanisms that give rise to germ bands with similar morphological pattern.


2021 ◽  
Author(s):  
Nathanael Andrews ◽  
Jason T. Serviss ◽  
Natalie Geyer (Karolinska Institute Stockholm) ◽  
Agneta B. Andersson ◽  
Ewa Dzwonkowska ◽  
...  

Single cell sequencing methods facilitate the study of tissues at high resolution, revealing rare cell types with varying transcriptomes or genomes, but so far have been lacking the capacity to investigate cell-cell interactions. Here, we introduce CIM-seq, an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between every cell type in a given tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution of these into their constituent cell types using machine learning. CIM-seq is broadly applicable to studies that aim to simultaneously investigate the constituent cell types and the global interaction profile in a specific tissue.


Cell Systems ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 496-507.e6 ◽  
Author(s):  
Simon van Vliet ◽  
Alma Dal Co ◽  
Annina R. Winkler ◽  
Stefanie Spriewald ◽  
Bärbel Stecher ◽  
...  

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