Transitions from mono- to co- to tri-culture uniquely affect gene expression in breast cancer, stromal, and immune compartments

Abstract

Heterotypic interactions in cancer microenvironments play important roles in disease initiation, progression, and spread. Co-culture is the predominant approach used in dissecting paracrine interactions between tumor and stromal cells, but functional results from simple co-cultures frequently fail to correlate to in vivo conditions. Though complex heterotypic in vitro models have improved functional relevance, there is little systematic knowledge of how multi-culture parameters influence this recapitulation. We therefore have employed a more iterative approach to investigate the influence of increasing model complexity; increased heterotypic complexity specifically. Here we describe how the compartmentalized and microscale elements of our multi-culture device allowed us to obtain gene expression data from one cell type at a time in a heterotypic culture where cells communicated through paracrine interactions. With our device we generated a large dataset comprised of cell type specific gene-expression patterns for cultures of increasing complexity (three cell types in mono-, co-, or tri-culture) not readily accessible in other systems. Principal component analysis indicated that gene expression was changed in co-culture but was often more strongly altered in tri-culture as compared to mono-culture. Our analysis revealed that cell type identity and the complexity around it (mono-, co-, or tri-culture) influence gene regulation. We also observed evidence of complementary regulation between cell types in the same heterotypic culture. Here we demonstrate the utility of our platform in providing insight into how tumor and stromal cells respond to microenvironments of varying complexities highlighting the expanding importance of heterotypic cultures that go beyond conventional co-culture.

Keywords: Compartmentalization; Heterotypic interactions; Microfluidic; Multi-culture; Principal component analysis.

Fig. 1

Schematic drawing of the compartmentalized micro multi-culture platform and validation of limited cross-contamination. a The micro multiculture device consists of five culture chambers separated by diffusion ports. The device holds a total volume of 50 μL with each culture chamber containing 4 μL. b Cells are seeded into the individual compartments through the chamber input port. Media was changed by replacing the media in the outer ring or in each chamber input port. c Green food grade dye was used to fill the device (50 μL) then 4 μL red dye was pipetted into the center chamber, and 4 μL purple dye was pipetted into the left culture chambers for visualization of addressability of individual compartments. d Fluorescence micrograph of MCF7eGFP cells in the center chamber with HS5 cells in all four side chambers demonstrating maintenance of cell compartmentalization. e Percentage of carryover of cellular lysate in the culture chambers. eGFP mRNA expression levels were measured in HS5 samples after co-culture with MCF7eGFP cells, percentage of carryover over was determined by comparing eGFP expression detected in HS5 cells relative to MCF7eGFP cells at the intermediate time point of 48 h. Between <0.5 % - 6 % cross-contamination was detected, indicating the compartmentalized micro multi-culture device can effectively isolate cellular lysate from the individual culture chambers without the need for downstream separation techniques (n = 2). f Fluorescence micrograph of HS5 cells in the side chambers after MCF7eGFP cells have been selectively removed

Fig. 2

Relative gene expression of target genes of interest across experimental conditions for experiments utilizing SKBR3, HS5, and ThP1-M2 cells with a 72-h endpoint. Gene expression levels of selected target genes known to be dysregulated in cancer were measured using RT-qPCR. The heat-map was generated by normalizing the expression of each gene to a reference gene and to monoculture for the expressing cell type and calculating the fold-change using the 2^-ΔΔCt method. Fold changes are listed in the heat-map for each condition and target gene with a false reference. Red indicates up-regulation, green down-regulation, and black no change in expression detected. Note: Throughout the figures the cell types in co- or tri-culture are listed and separated by slashes to indicate the configuration of the experiment. To indicate which cell type a gene regulation profile corresponds to, the expressing cell-type is listed first (ie SK/HS is a dataset derived from SKBR3 lysate in a configuration where the SKBR3 cells were co-cultured with HS5 cells) with all other cell types in the culture condition listed after the assayed cell type and separated by slashes.

Fig. 3

PCA explained variation plot depicting the individual (bar) and cumulative (line) explained variation for each principal component when considering all culture conditions and transcripts

Fig. 4

PCA summary figures depicting, a bar graphs containing the scores for each culture condition and loadings for each transcript in the first two principle components (Comp 1&2) b the scores for all nine heterotypic culture gene expression profiles normalized to monoculture plotted with the 95 % confidence Hotelling’s T² ellipse, and c a biplot depicting the relationships between variable loadings and the scaled observation scores from b

Fig. 5

Matrix containing Pearson’s correlation rho values for the mean centered, unit variance scaled gene regulation (fold change) profiles of each culture condition compared with each other culture condition in the SKBR3 72-h dataset for comparison of gene expression changes in response to co- or tri-culture conditions. Significant (p < 0.05) rho values are bolded, italicized, and underlined