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Abstract Most solid tumors become stiff with progression of cancer. Cancer Associated Fibroblasts (CAFs), most abundant stromal cells in the tumor microenvironment (TME), are known to mediate such stiffening. While the biochemical crosstalk between CAFs and cancer cells have been widely investigated, it is not clear if and how CAFs in stiffer TME promote metastatic progression. To gather insights into the process, we controlled the mechanical stiffness of the substrates and collected gene expression data with human colorectal CAFs. We cultured human primary CAFs on 2D polyacrylamide hydrogels with increasing elastic modulus (E) of 1, 10 and 40 kPa, and performed genome-wide transcriptome analyses in these cells to identify expression levels of ~16000 genes. The high-quality RNAseq results can be an excellent data-source for bioinformatic analysis for identifying novel pathways and biomarkers in cancer development and metastatic progression. With thorough analysis and accurate interpretation, this data may help researchers understand the role of mechanical stiffness of the TME in CAF-cancer cell crosstalk. 

Abstract Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable. 

Tissue-on-chip systems represent promising platforms for monitoring and controlling tissue functions in vitro for various purposes in biomedical research. The two-dimensional (2D) layouts of these constructs constrain the types of interactions that can be studied and limit their relevance to three-dimensional (3D) tissues. The development of 3D electronic scaffolds and microphysiological devices with geometries and functions tailored to realistic 3D tissues has the potential to create important possibilities in advanced sensing and control. This study presents classes of compliant 3D frameworks that incorporate microscale strain sensors for high-sensitivity measurements of contractile forces of engineered optogenetic muscle tissue rings, supported by quantitative simulations. Compared with traditional approaches based on optical microscopy, these 3D mechanical frameworks and sensing systems can measure not only motions but also contractile forces with high accuracy and high temporal resolution. Results of active tension force measurements of engineered muscle rings under different stimulation conditions in long-term monitoring settings for over 5 wk and in response to various chemical and drug doses demonstrate the utility of such platforms in sensing and modulation of muscle and other tissues. Possibilities for applications range from drug screening and disease modeling to biohybrid robotic engineering.