Synthetic development is a nascent field of research that uses the tools of synthetic biology to design genetic programs directing cellular patterning and morphogenesis in higher eukaryotic cells, such as mammalian cells. One specific example of such synthetic genetic programs was based on cell–cell contact-dependent signaling using synthetic Notch pathways and was shown to drive the formation of multilayered spheroids by modulating cell–cell adhesion via differential expression of cadherin family proteins in a mouse fibroblast cell line (L929). The design method for these genetic programs relied on trial and error, which limited the number of possible circuits and parameter ranges that could be explored. Here, we build a parameterized computational framework that, given a cell–cell communication network driving changes in cell adhesion and initial conditions as inputs, predicts developmental trajectories. We first built a general computational framework where contact-dependent cell–cell signaling networks and changes in cell–cell adhesion could be designed in a modular fashion. We then used a set of available in vitro results (that we call the “training set” in analogy to similar pipelines in the machine learning field) to parameterize the computational model with values for adhesion and signaling. We then show that this parameterized model can qualitatively predict experimental results from a “testing set” of available in vitro data that varied the genetic network in terms of adhesion combinations, initial number of cells, and even changes to the network architecture. Finally, this parameterized model is used to recommend novel network implementation for the formation of a four-layered structure that has not been reported previously. The framework that we develop here could function as a testing ground to identify the reachable space of morphologies that can be obtained by controlling contact-dependent cell–cell communications and adhesion with these molecular tools and in this cellular system. Additionally, we discuss how the model could be expanded to include other forms of communication or effectors for the computational design of the next generation of synthetic developmental trajectories.

Urinary bladder cancer (BCa) is the 10th most frequent cancer in the world, most commonly found among the elderly population, and becomes highly lethal once cells have spread from the primary tumor to surrounding tissues and distant organs. Cystectomy, alone or with other treatments, is used to treat most BCa patients, as it offers the best chance of cure. However, even with curative intent, 29% of patients experience relapse of the cancer, 50% of which occur within the first year of surgery. This study aims to use the liquid biopsy to noninvasively detect disease and discover prognostic markers for disease progression. Using the third generation high-definition single cell assay (HDSCA3.0), 50 bladder cancer patient samples and 50 normal donor (ND) samples were analyzed for circulating rare events in the peripheral blood (PB), including circulating tumor cells (CTCs) and large extracellular vesicles (LEVs). Here, we show that (i) CTCs and LEVs are detected in the PB of BCa patients prior to cystectomy, (ii) there is a high heterogeneity of CTCs, and (iii) liquid biopsy analytes correlate with clinical data elements. We observed a significant difference in the incidence of rare cells and LEVs between BCa and ND samples (median of 74.61 cells/mL and 30.91 LEVs/mL vs. 34.46 cells/mL and 3.34 LEVs/mL, respectively). Furthermore, using classification models for the liquid biopsy data, we achieved a sensitivity of 78% and specificity of 92% for the identification of BCa patient samples. Taken together, these data support the clinical utility of the liquid biopsy in detecting BCa, as well as the potential for predicting cancer recurrence and survival post-cystectomy to better inform treatment decisions in BCa care.

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Urothelial carcinomas (UCs) are a broad and heterogeneous group of malignancies, with the prevalence of upper tract urothelial carcinoma (UTUC) being rare, accounting for only 5–10% of total malignancies. There is a need for additional toolsets to assist the current clinical paradigm of care for patients with UTUC. As a non-invasive tool for the discovery of cancer-related biomarkers, the liquid biopsy has the potential to represent the complex process of tumorigenesis and metastasis. Herein, we show the efficacy of the liquid biopsy as a source of biomarkers for detecting UTUC. Using the third-generation high-definition single-cell assay (HDSCA3.0) workflow, we investigate liquid biopsy samples collected from patients with UTUC and normal donors (NDs) to provide critical information regarding the molecular and morphological characteristics of circulating rare events. We document several important findings from the liquid biopsy analysis of patients diagnosed with UTUC prior to surgery: (1) Large extracellular vesicles (LEVs) and circulating tumor cells (CTCs) are detectable in the peripheral blood. (2) The rare-event profile is highly heterogeneous. (3) Clinical data elements correlate with liquid biopsy analytes. Overall, this study provides evidence for the efficacy of the liquid biopsy in understanding the biology of UTUC with the future intent of informing clinical decision making, ultimately improving patient outcomes.