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Generation of neighbor-labeling cells to study intercellular interactions in vivo.

ABSTRACT: Understanding cell-cell interactions is critical in most, if not all, research fields in biology. Nevertheless, studying intercellular crosstalk in vivo remains a relevant challenge, due mainly to the difficulty in spatially locating the surroundings of particular cells in the tissue. Cherry-niche is a powerful new method that enables cells expressing a fluorescent protein to label their surrounding cells, facilitating their specific isolation from the whole tissue as live cells. We previously applied Cherry-niche in cancer research to study the tumor microenvironment (TME) in metastasis. Here we describe how to generate cancer cells with the ability to label their neighboring cells (within the tumor niche) by transferring a liposoluble fluorescent protein. Live niche cells can be isolated and compared with cells distant from the tumor bulk, using a variety of ex vivo approaches. As previously shown, this system has the potential to identify novel components in the TME and improve our understanding of their local interactions. Importantly, Cherry-niche can also be applied to study potential cell-cell interactions due to in vivo proximity in research fields beyond cancer. This protocol takes 2-3 weeks to generate the labeling cells and 1-2 weeks to test their labeling ability.

SUBMITTER: Ombrato L

PROVIDER: S-EPMC7615221 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Generation of neighbor-labeling cells to study intercellular interactions in vivo.

Ombrato Luigi L Nolan Emma E Passaro Diana D Kurelac Ivana I Bridgeman Victoria L VL Waclawiczek Alexander A Duarte Delfim D Lo Celso Cristina C Bonnet Dominique D Malanchi Ilaria I

Nature protocols 20201211 2

Understanding cell-cell interactions is critical in most, if not all, research fields in biology. Nevertheless, studying intercellular crosstalk in vivo remains a relevant challenge, due mainly to the difficulty in spatially locating the surroundings of particular cells in the tissue. Cherry-niche is a powerful new method that enables cells expressing a fluorescent protein to label their surrounding cells, facilitating their specific isolation from the whole tissue as live cells. We previously a ...[more]

PMID: 33311715

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Project description:Modulation of interspecies interactions by the presence of neighbor species is a key ecological factor that governs dynamics and function of microbial communities, yet the development of theoretical frameworks explicit for understanding context-dependent interactions are still nascent. In a recent study, we proposed a novel rule-based inference method termed the Minimal Interspecies Interaction Adjustment (MIIA) that predicts the reorganization of interaction networks in response to the addition of new species such that the modulation in interaction coefficients caused by additional members is minimal. While the theoretical basis of MIIA was established through the previous work by assuming the full availability of species abundance data in axenic, binary, and complex communities, its extension to actual microbial ecology can be highly constrained in cases that species have not been cultured axenically (e.g., due to their inability to grow in the absence of specific partnerships) because binary interaction coefficients - basic parameters required for implementing the MIIA - are inestimable without axenic and binary population data. Thus, here we present an alternative formulation based on the following two central ideas. First, in the case where only data from axenic cultures are unavailable, we remove axenic populations from governing equations through appropriate scaling. This allows us to predict neighbor-dependent interactions in a relative sense (i.e., fractional change of interactions between with versus without neighbors). Second, in the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. Through the case study of two microbial communities with distinct characteristics and complexity (i.e., a three-member community where all members can grow independently, and a four-member community that contains member species whose growth is dependent on other species), we demonstrated that despite data limitation, the proposed new formulation was able to successfully predict interspecies interactions that are consistent with experimentally derived results. Therefore, this technical advancement enhances our ability to predict context-dependent interspecies interactions in a broad range of microbial systems without being limited to specific growth conditions as a pre-requisite.

Dataset Information

Generation of neighbor-labeling cells to study intercellular interactions in vivo.

Publications

Generation of neighbor-labeling cells to study intercellular interactions in vivo.

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