3D bioprinting field is making remarkable progress; however, the development of critical sized engineered tissue construct is still a farfetched goal. Silk fibroin offers a promising choice for bioink material. Nature has imparted several unique structural features in silk protein to ensure spinnability by silkworms or spider. Researchers have modified the structure-property relationship by reverse engineering to further improve shear thinning behavior, high printability, cytocompatible gelation, and high structural fidelity. In this review, it is attempted to summarize the recent advancements made in the field of 3D bioprinting in context of two major sources of silk fibroin: silkworm silk and spider silk (native and recombinant). The challenges faced by current approaches in processing silk bioinks, cellular signaling pathways modulated by silk chemistry and secondary conformations, gaps in knowledge, and future directions acquired for pushing the field further toward clinic are further elaborated.

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Human hair dermal papilla (DP) cells are specialized mesenchymal cells that play a pivotal role in hair regeneration and hair cycle activation. The current study aimed to first develop three-dimensional (3D) DP spheroids (DPS) with or without a silk-gelatin (SG) microenvironment, which showed enhanced DP-specific gene expression, resulting in enhanced extracellular matrix (ECM) production compared with a monolayer culture. We tested the feasibility of using this DPS model for drug screening by using minoxidil, which is a standard drug for androgenic alopecia. Minoxidil-treated DPS showed enhanced expression of growth factors and ECM proteins. Further, an attempt has been made to establish an in vitro 3D organoid model consisting of DPS encapsulated by SG hydrogel and hair follicle (HF) keratinocytes and stem cells. This HF organoid model showed the importance of structural features, cell-cell interaction, and hypoxia akin to in vivo HF. The study helped to elucidate the molecular mechanisms to stimulate cell proliferation, cell viability, and elevated expression of HF markers as well as epithelial-mesenchymal crosstalks, demonstrating high relevance to human HF biology. This simple in vitro DP organoid model system has the potential to provide significant insights into the underlying mechanisms of HF morphogenesis, distinct molecular signals relevant to different stages of the hair cycle, and hence can be used for controlled evaluation of the efficacy of new drug molecules.

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection.