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Tools for tuning transcription in mammalian cells have broad applications, from basic biological discovery to human gene therapy. While precise control over target gene transcription via dosing with small molecules (drugs) is highly sought, the design of such inducible systems that meets required performance metrics poses a great challenge in mammalian cell synthetic biology. Important characteristics include tight and tunable gene expression with a low background, minimal drug toxicity, and orthogonality. Here, we review small-molecule-inducible transcriptional control devices that have demonstrated success in mammalian cells and mouse models. Most of these systems employ natural or designed ligand-binding protein domains to directly or indirectly communicate with transcription machinery at a target sequence, via carefully constructed fusions. Example fusions include those to transcription activator-like effectors (TALEs), DNA-targeting proteins (e.g. dCas systems) fused to transactivating domains, and recombinases. Similar to the architecture of Type I nuclear receptors, many of the systems are designed such that the transcriptional controller is excluded from the nucleus in the absence of an inducer. Techniques that use ligand-induced proteolysis and antibody-based chemically induced dimerizers are also described. Collectively, these transcriptional control devices take advantage of a variety of recently developed molecular biology tools and cell biology insights and represent both proof of concept (e.g. targeting reporter gene expression) and disease-targeting studies. 

Background Adoptive cell therapy based on the infusion of chimeric antigen receptor (CAR) T cells has shown remarkable efficacy for the treatment of hematologic malignancies. The primary mechanism of action of these infused T cells is the direct killing of tumor cells expressing the cognate antigen. However, understanding why only some T cells are capable of killing, and identifying mechanisms that can improve killing has remained elusive. Methods To identify molecular and cellular mechanisms that can improve T-cell killing, we utilized integrated high-throughput single-cell functional profiling by microscopy, followed by robotic retrieval and transcriptional profiling. Results With the aid of mathematical modeling we demonstrate that non-killer CAR T cells comprise a heterogeneous population that arise from failure in each of the discrete steps leading to the killing. Differential transcriptional single-cell profiling of killers and non-killers identified CD137 as an inducible costimulatory molecule upregulated on killer T cells. Our single-cell profiling results directly demonstrate that inducible CD137 is feature of killer (and serial killer) T cells and this marks a different subset compared with the CD107a pos (degranulating) subset of CAR T cells. Ligation of the induced CD137 with CD137 ligand (CD137L) leads to younger CD19 CAR T cells with sustained killing and lower exhaustion. We genetically modified CAR T cells to co-express CD137L, in trans, and this lead to a profound improvement in anti-tumor efficacy in leukemia and refractory ovarian cancer models in mice. Conclusions Broadly, our results illustrate that while non-killer T cells are reflective of population heterogeneity, integrated single-cell profiling can enable identification of mechanisms that can enhance the function/proliferation of killer T cells leading to direct anti-tumor benefit. 

The protein p53 is a crucial tumor suppressor, often called “the guardian of the genome”; however, mutations transform p53 into a powerful cancer promoter. The oncogenic capacity of mutant p53 has been ascribed to enhanced propensity to fibrillize and recruit other cancer fighting proteins in the fibrils, yet the pathways of fibril nucleation and growth remain obscure. Here, we combine immunofluorescence three-dimensional confocal microscopy of human breast cancer cells with light scattering and transmission electron microscopy of solutions of the purified protein and molecular simulations to illuminate the mechanisms of phase transformations across multiple length scales, from cellular to molecular. We report that the p53 mutant R248Q (R, arginine; Q, glutamine) forms, both in cancer cells and in solutions, a condensate with unique properties, mesoscopic protein-rich clusters. The clusters dramatically diverge from other protein condensates. The cluster sizes are decoupled from the total cluster population volume and independent of the p53 concentration and the solution concentration at equilibrium with the clusters varies. We demonstrate that the clusters carry out a crucial biological function: they host and facilitate the nucleation of amyloid fibrils. We demonstrate that the p53 clusters are driven by structural destabilization of the core domain and not by interactions of its extensive unstructured region, in contradistinction to the dense liquids typical of disordered and partially disordered proteins. Two-step nucleation of mutant p53 amyloids suggests means to control fibrillization and the associated pathologies through modifying the cluster characteristics. Our findings exemplify interactions between distinct protein phases that activate complex physicochemical mechanisms operating in biological systems.

 

Time lapse microscopy is essential for quantifying the dynamics of cells, subcellular organelles and biomolecules. Biologists use different fluorescent tags to label and track the subcellular structures and biomolecules within cells. However, not all of them are compatible with time lapse imaging, and the labeling itself can perturb the cells in undesirable ways. We hypothesized that phase image has the requisite information to identify and track nuclei within cells. By utilizing both traditional blob detection to generate binary mask labels from the stained channel images and the deep learning Mask RCNN model to train a detection and segmentation model, we managed to segment nuclei based only on phase images. The detection average precision is 0.82 when the IoU threshold is to be set 0.5. And the mean IoU for masks generated from phase images and ground truth masks from experts is 0.735. Without any ground truth mask labels during the training time, this is good enough to prove our hypothesis. This result enables the ability to detect nuclei without the need for exogenous labeling.