The study of the liver progenitor cell microenvironment has demonstrated the important roles of both biochemical and biomechanical signals in regulating the progenitor cell functions that underlie liver morphogenesis and regeneration. While controllable twodimensional in vitro culture systems have provided key insights into the effects of growth factors and extracellular matrix composition and mechanics on liver differentiation, it remains unclear how microenvironmental signals may differentially affect liver progenitor cell responses in a three-dimensional (3D) culture context. In addition, there have only been limited efforts to engineer 3D culture models of liver progenitor cells through the tunable presentation of microenvironmental stimuli. We present an in vitro model of 3D liver progenitor spheroidal cultures with integrated polyethylene glycol hydrogel microparticles for the internal presentation of modular microenvironmental cues and the examination of the combinatorial effects with an exogenous soluble factor. In particular, treatment with the growth factor TGFβ1 directs differentiation of the spheroidal liver progenitor cells toward a biliary phenotype, a behavior which is further enhanced in the presence of hydrogel microparticles. We further demonstrate that surface modification of the hydrogel microparticles with heparin influences the behavior of liver progenitor cells toward biliary differentiation. Taken together, this liver progenitor cell culture system represents an approach for controlling the presentation of microenvironmental cues internalized within 3D spheroidal aggregate cultures. Overall, this strategy could be applied toward the engineering of instructive microenvironments that control stem and progenitor cell differentiation within a 3D context for studies in tissue engineering, drug testing, and cellular metabolism.

Carcinoma progression is influenced by interactions between epithelial tumor cells and components of their microenvironment. In particular, cell-extracellular matrix (ECM) interactions are known to drive tumor growth, metastatic potential, and sensitivity or resistance to therapy. Yet the intrinsic complexity of ECM composition within the tumor microenvironment remains a barrier to comprehensive investigation of these interactions. We present here a high-throughput cell microarray-based approach to study the impact of defined combinations of ECM proteins on tumor cell drug responses. Using this approach, we quantitatively evaluated the effects of 55 different ECM environments representing all single and two-factor combinations of 10 ECM proteins on the responses of lung adenocarcinoma cells to a selection of cancer-relevant small molecule drugs. This drug panel consisted of an alkylating agent and five receptor tyrosine kinase inhibitors. We further determined that expression of the neuroendocrine transcription factor ASCL1, which has been previously associated with poor patient outcome when co-expressed with the RET oncogene, altered cell responses to drugs and modulated cleavage of the pro-apoptotic protein caspase-3 depending on ECM context. Our results suggest that co-expression of specific ECM proteins with known genetic drivers in lung adenocarcinoma may impact therapeutic efficacy. Furthermore, this approach could be utilized to define the molecular mechanisms by which cell-matrix interactions drive drug resistance through integration with clinical cell samples and genomics data.

Cell labeling and tracking methodologies can play an important role in experiments aimed at understanding biological systems. However, many current cell labeling and tracking techniques have limitations that preclude their use in a variety of multiplexed and high-throughput applications that could best represent the heterogeneity and combinatorial complexity present in physiologic contexts. Here, we demonstrate an approach for labeling, tracking, and quantifying cells using double-stranded DNA barcodes. These barcodes are introduced to the outside of the cell membrane, giving the labeled cells a unique identifier. This approach is compatible with flow cytometric and PCR-based identification and relative quantification of the presence of barcode-labeled cells. Further, utilizing this strategy, we demonstrate the capacity for sorting and enrichment of barcoded cells from a bulk population. In addition, we illustrate the design and utility of a range of orthogonal barcode sequences, which can enable the use of multiple independent barcodes to track, sort, and enrich multiple cell types and/or cells receiving distinct treatments from a pooled sample. Overall, this method of labeling cells has the potential to track multiple populations of cells in both high-throughput in vitro and physiologic in vivo settings.