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. 2015 Feb 17;112(7):2287-92.
doi: 10.1073/pnas.1410776112. Epub 2015 Jan 29.

A strategy for tissue self-organization that is robust to cellular heterogeneity and plasticity

Alec E Cerchiari  1 , James C Garbe  2 , Noel Y Jee  3 , Michael E Todhunter  3 , Kyle E Broaders  3 , Donna M Peehl  4 , Tejal A Desai  5 , Mark A LaBarge  6 , Matthew Thomson  7 , Zev J Gartner  8
Affiliations

A strategy for tissue self-organization that is robust to cellular heterogeneity and plasticity

Alec E Cerchiari et al. Proc Natl Acad Sci U S A. .

Abstract

Developing tissues contain motile populations of cells that can self-organize into spatially ordered tissues based on differences in their interfacial surface energies. However, it is unclear how self-organization by this mechanism remains robust when interfacial energies become heterogeneous in either time or space. The ducts and acini of the human mammary gland are prototypical heterogeneous and dynamic tissues comprising two concentrically arranged cell types. To investigate the consequences of cellular heterogeneity and plasticity on cell positioning in the mammary gland, we reconstituted its self-organization from aggregates of primary cells in vitro. We find that self-organization is dominated by the interfacial energy of the tissue-ECM boundary, rather than by differential homo- and heterotypic energies of cell-cell interaction. Surprisingly, interactions with the tissue-ECM boundary are binary, in that only one cell type interacts appreciably with the boundary. Using mathematical modeling and cell-type-specific knockdown of key regulators of cell-cell cohesion, we show that this strategy of self-organization is robust to severe perturbations affecting cell-cell contact formation. We also find that this mechanism of self-organization is conserved in the human prostate. Therefore, a binary interfacial interaction with the tissue boundary provides a flexible and generalizable strategy for forming and maintaining the structure of two-component tissues that exhibit abundant heterogeneity and plasticity. Our model also predicts that mutations affecting binary cell-ECM interactions are catastrophic and could contribute to loss of tissue architecture in diseases such as breast cancer.

Keywords: cell sorting; differential adhesion; heterogeneity; mammary; prostate.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
A self-generated and binary adhesive interaction directs cell positioning in the mammary epithelium. (A) Self-organization of two initially disordered populations of cells (Center) into spatially ordered tissues. In the mammary gland, the correct architecture (Right) can go on to polarize and form a lumen. (B) Self-organization of fourth-passage primary human mammary epithelial cells in agarose (Left) and Matrigel (Right) after 24 h. In Matrigel, the reconstituted microtissue can also polarize and form a lumen over an additional 72 h (MEP, red, keratin-14/K14; LEP, green, keratin-19/K19; blue, DAPI/nuclei). (C) Experiments as in B but with MEP and LEP stained before self-organization with CellTracker Red (CTR) and CellTracker Green (CTG), respectively. (Insets) Average intensity profiles under each condition (n = 30). (D) Frequency of indicated tissue architectures for experiments in C (n > 235). (E) Representative images and average intensity plots of CellTracker-labeled MEP and LEP self-organized in Col1-functionalized agarose (n = 30) and unfunctionalized PDMS microwells (n = 20). (F) Conceptual model for self-organization by a self-generated adhesive interaction at the tissue–ECM boundary. (Inset) An image of MEP on an unfunctionalized PDMS surface (dotted line, PDMS; yellow, fibronectin-1; red, actin; blue, nuclei). (G) Representative images of cell doublets and XZ sections of single cells after 4 h on Matrigel-coated substrate (green, CTG; red, CTR; purple, QD605). (H) Distribution of measured contact angles at all interfaces (n > 42). (I) Representative images of aggregates of homogeneous MEP and LEP after 12 h in agarose wells. (J) Aggregates prepared as in G but subsequently transferred to Matrigel-coated glass for 12 h (green, K19; red, K14). Error bars are SD. (Scale bars, 10 µm.)
Fig. 2.
Fig. 2.
A lattice-based model of self-organization predicts robustness to perturbations affecting cell–cell cohesion in the presence of an adhesive tissue boundary. (A) Two configurations of LEP (green), MEP (red), and ECM (black) with different stabilities. Numbers on edges represent the strength (relative to LEP–LEP) of specific interactions. Larger numbers represent more favorable interactions. (B) Output of Monte-Carlo simulations using the indicated values for WMEP-ECM for tissue self-organization on a square lattice with stationary ECM. (C) Phase diagrams for tissue self-organization in the presence (Top) and absence (Bottom) of MEP–ECM interactions. Each sphere represents a single run of the model. Color represents the given tissue architecture (small icons). (D) Cross-sections through the phase diagrams in C reveal the combinations of parameters representative of fourth-passage human primary mammary epithelial cells in the presence of ECM (i). Positions ii–iv represent predicted tissue phases upon specific perturbations described in the text.
Fig. 3.
Fig. 3.
Self-organization is robust to perturbation of cell–cell cohesion only in the presence of an adhesive tissue boundary. (A) Representative images of MEP used to measure contact angles at the cell–cell and cell–ECM interfaces for the given perturbations. (B) Quantification of MEP contact angles (and SD) at the cell–cell and (C) cell–ECM interfaces (n = 16–56) for the given perturbations. Measurements for Talin1 knockdown cells do not account for a significant fraction of the population that do not adhere to matrix and are removed during wash steps. (D) Representative image and average intensity plots (Inset, n = 20) for p120 knockdown MEP self-organized with control LEP in agarose and (E) Matrigel. (F) Distributions of tissue architectures from D and E (n = 45–54). (G) Representative image and average intensity profiles (Inset, n = 20) for Talin1 knockdown MEP with control LEP in agarose and (H) Matrigel. (I) Distributions of tissue architectures from G and H (n = 72–81). Red, CellTracker Red; green, CellTracker Green. (Scale bars, 10 µm.)
Fig. 4.
Fig. 4.
An adhesive tissue boundary supports self-organization among populations of cells that are heterogeneous in their cohesive properties. (A) Circularity of pure aggregates of uncultured human primary mammary LEP and MEP (n > 23). (B) Aggregate spreading assay of pure uncultured LEP and MEP. (C) Representative images and average keratin intensity profiles (Inset, n = 20) for uncultured primary human mammary epithelial cells self-organizing in agarose (Left) and Matrigel (Right). Red, K14; green, K19. (D) Distribution of tissue architectures for self-organizing uncultured primary cells in Matrigel (gray, n = 53) and agarose (orange, n = 56). (E) Log-normal homotypic interaction energy distributions for LEP (green) and MEP (red) (σ/dμ = 1.5). (F) Simulated self-organization of LEP (green) and MEP (red) in agarose (Left) and Matrigel (Right), but with σ/dµ = 1.5. (G) The relative efficiency of self-organization to an identical tissue architecture (configuration I) as a function of cell-to-cell variability using a strategy of binary cell–ECM adhesion (black; WMEP–ECM = 2 × WMEP–MEP) or differential cell–cell cohesion alone (orange). (Scale bars, 10 µm.)
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