Evan Appleton

Biodiversity loss in the present era requires new tools for studying nonmodel organisms. Elephants are both an endangered species and excellent models for studying complex phenotypes including size, social behavior and longevity. Here we report the first derivation of elephant (Elephas maximus) induced pluripotent stem (emiPS) cells. We achieved emiPS cells using two approaches: (1) a two-step process of chemical media induction and colony selection followed by over-expression of elephant transcription factors; and (2) a one-step process with transcription factors and HRAS mutant, HRAS_G12V. For both protocols, we inhibited TP53 retrogenes, which are hypothesized to confer unique cancer resistance in elephants. To confirm their reprogrammed state, we generated a functional omics catalog of emiPS cells. While these emiPS cells remain transgene-dependent, we inactivated the transgenes and differentiated emiPS cells into all three germ layers via tri-lineage differentiation, embryoid body generation and direct differentiation into putative cell types from all three layers. These methods will open new frontiers for cellular models of nonmodel organisms, including for genetic rescue and conservation.

Differentiation of induced pluripotent stem cells (iPSCs) into specialized cell types is essential for uncovering cell-type specific molecular mechanisms and interrogating cellular function. Transcription factor screens have enabled efficient production of a few cell types; however, engineering cell types that require complex transcription factor combinations remains challenging. Here, we report an iterative, high-throughput single-cell transcription factor screening method that enables the identification of transcription factor combinations for specialized cell differentiation, which we validated by differentiating human microglia-like cells. We found that the expression of six transcription factors, SPI1, CEBPA, FLI1, MEF2C, CEBPB, and IRF8, is sufficient to differentiate human iPSC into cells with transcriptional and functional similarity to primary human microglia within 4 days. Through this screening method, we also describe a novel computational method allowing the exploration of single-cell RNA sequencing data derived from transcription factor perturbation assays to construct causal gene regulatory networks for future cell fate engineering.

Multicellular organisms originate from a single cell, ultimately giving rise to mature organisms of heterogeneous cell type composition in complex structures. Recent work in the areas of stem cell biology and tissue engineering has laid major groundwork in the ability to convert certain types of cells into other types, but there has been limited progress in the ability to control the morphology of cellular masses as they grow. Contemporary approaches to this problem have included the use of artificial scaffolds, 3D bioprinting, and complex media formulations; however, there are no existing approaches to controlling this process purely through genetics and from a single-cell starting point. Here we describe a computer-aided design approach, called CellArchitect, for designing recombinase-based genetic circuits for controlling the formation of multicellular masses into arbitrary shapes in human cells.

The crisis of biodiversity loss in the anthropogenic era requires new tools for studying non-model organisms. Elephants, for example, are both an endangered species and excellent models studying complex phenotypes like size, social behavior, and longevity, but they remain severely understudied. Here we report the first derivation of elephant ( Elephas maximus ) induced pluripotent stem cells (emiPSCs) achieved via a two-step process of chemical-media induction and colony selection, followed by overexpression of elephant transcription factors OCT4, SOX2, KLF4, MYC ± NANOG and LIN28A , and modulation of the TP53 pathway. Since the seminal discovery of reprogramming by Shinya Yamanaka, iPSCs from many species including the functionally extinct northern white rhinocerous have been reported, but emiPSCs have remained elusive. While for multiple species the reprogramming protocol was adopted with little changes compared to model organisms like mouse and human, our emiPSC protocol requires a longer timeline and inhibition of TP53 expansion genes that are hypothesized to confer unique cancer resistance in elephants. iPSCs unlock tremendous potential to explore cell fate determination, cell and tissue development, cell therapies, drug screening, disease modeling, cancer development, gametogenesis and beyond to further our understanding of this iconic megafauna. This study opens new frontiers in advanced non-model organism cellular models for genetic rescue and conservation.

A bstract: The creation of induced pluripotent stem cells (iPSCs) has enabled scientists to explore the derivation of many types of cells. While there are diverse general approaches for cell-fate engineering, one of the fastest and most efficient approaches is transcription factor (TF) over-expression. However, finding the right combination of TFs to over-express to differentiate iPSCs directly into other cell-types is a difficult task. Here were describe a machine-learning (ML) pipeline, called CellCartographer , for using chromatin accessibility data to design multiplex TF pooled-screens for cell type conversions. We validate this method by differentiating iPSCs into twelve diverse cell types at low efficiency in preliminary screens and then iteratively refining our TF combinations to achieve high efficiency differentiation for six of these cell types in < 6 days. Finally, we functionally characterized engineered iPSC-derived cytotoxic T-cells (iCytoT), regulatory T-cells (iTReg), type II astrocytes (iAstII), and hepatocytes (iHep) to validate functionally accurate differentiation.

Summary: Interactions between cells are indispensable for creating structure and signaling. The ability to direct precise cell-cell interactions would be powerful for engineering tissues, understanding signaling pathways, and directing immune cell targeting. In humans, intercellular interactions are mediated by cell adhesion molecules (CAMs). However, CAMs are natively expressed by many cells and have cross-reactivity, making them unsuitable for programming specific interactions. Here, we showcase “helixCAM,” a platform for engineering novel CAMs by presenting coiled-coil peptides on the cell surface. helixCAMs were able to create specific cell-cell interactions and direct patterned aggregate formation in bacteria and human cells. We built rationally designed helixCAM libraries and discovered novel high-performance helixCAM pairs. High-affinity helixCAMs were then used for multicellular engineering applications, such as spherical layering, adherent cell targeting, and surface patterning. The helixCAMs are an expandable platform for directing complex multicellular assemblies, and we foresee its utility in tissue engineering, immunology, and developmental biology.

Understanding the evolutionary stability and possible context dependence of biological containment techniques is critical as engineered microbes are increasingly under consideration for applications beyond biomanufacturing. While synthetic auxotrophy previously prevented Escherichia coli from exhibiting detectable escape from batch cultures, its long-term effectiveness is unknown. Here, we report automated continuous evolution of a synthetic auxotroph while supplying a decreasing concentration of essential biphenylalanine (BipA). After 100 days of evolution, triplicate populations exhibit no observable escape and exhibit normal growth rates at 10-fold lower BipA concentration than the ancestral synthetic auxotroph. Allelic reconstruction reveals the contribution of three genes to increased fitness at low BipA concentrations. Based on its evolutionary stability, we introduce the progenitor strain directly to mammalian cell culture and observe containment of bacteria without detrimental effects on HEK293T cells. Overall, our findings reveal that synthetic auxotrophy is effective on time scales and in contexts that enable diverse applications.

Human cell conversion technology has become an important tool for devising new cell transplantation therapies, generating disease models and testing gene therapies. However, while transcription factor over-expression-based methods have shown great promise in generating cell types in vitro, they often endure low conversion efficiency. In this context, great effort has been devoted to increasing the efficiency of current protocols and the development of computational approaches can be of great help in this endeavor. Here we introduce a computer-guided design tool that combines a computational framework for prioritizing more efficient combinations of instructive factors (IFs) of cellular conversions, called IRENE, with a transposon-based genomic integration system for efficient delivery. Particularly, IRENE relies on a stochastic gene regulatory network model that systematically prioritizes more efficient IFs by maximizing the agreement of the transcriptional and epigenetic landscapes between the converted and target cells. Our predictions substantially increased the efficiency of two established iPSC-differentiation protocols (natural killer cells and melanocytes) and established the first protocol for iPSC-derived mammary epithelial cells with high efficiency.