A Spatiotemporal and Machine-Learning Platform Accelerates the Manufacturing of hPSC-derived Esophageal Mucosa.

Human pluripotent stem cell-derived tissue engineering offers great promise in designer cell-based personalized therapeutics. To harness such potential, a broader approach requires a deeper understanding of tissue-level interactions. We previously developed a manufacturing system for the ectoderm-derived skin epithelium for cell replacement therapy. However, it remains challenging to manufacture the endoderm-derived esophageal epithelium, despite both possessing similar stratified structure. Here we employ single cell and spatial technologies to generate a spatiotemporal multi-omics cell atlas for human esophageal development. We illuminate the cellular diversity, dynamics and signal communications for the developing esophageal epithelium and stroma. Using the machine-learning based Manatee, we prioritize the combinations of candidate human developmental signals for in vitro derivation of esophageal basal cells. Functional validation of the Manatee predictions leads to a clinically-compatible system for manufacturing human esophageal mucosa. Our approach creates a versatile platform to accelerate human tissue manufacturing for future cell replacement therapies to treat human genetic defects and wounds.

Skin basal cell carcinomas assemble a pro-tumorigenic spatially organized and self-propagating Trem2+ myeloid niche.

Cancer immunotherapies have revolutionized treatment but have shown limited success as single-agent therapies highlighting the need to understand the origin, assembly, and dynamics of heterogeneous tumor immune niches. Here, we use single-cell and imaging-based spatial analysis to elucidate three microenvironmental neighborhoods surrounding the heterogeneous basal cell carcinoma tumor epithelia. Within the highly proliferative neighborhood, we find that TREM2+ skin cancer-associated macrophages (SCAMs) support the proliferation of a distinct tumor epithelial population through an immunosuppression-independent manner via oncostatin-M/JAK-STAT3 signaling. SCAMs represent a unique tumor-specific TREM2+ population defined by VCAM1 surface expression that is not found in normal homeostatic skin or during wound healing. Furthermore, SCAMs actively proliferate and self-propagate through multiple serial tumor passages, indicating long-term potential. The tumor rapidly drives SCAM differentiation, with intratumoral injections sufficient to instruct naive bone marrow-derived monocytes to polarize within days. This work provides mechanistic insights into direct tumor-immune niche dynamics independent of immunosuppression, providing the basis for potential combination tumor therapies.

A conserved YAP/Notch/REST network controls the neuroendocrine cell fate in the lungs.

The Notch pathway is a conserved cell-cell communication pathway that controls cell fate decisions. Here we sought to determine how Notch pathway activation inhibits the neuroendocrine cell fate in the lungs, an archetypal process for cell fate decisions orchestrated by Notch signaling that has remained poorly understood at the molecular level. Using intratumoral heterogeneity in small-cell lung cancer as a tractable model system, we uncovered a role for the transcriptional regulators REST and YAP as promoters of the neuroendocrine to non-neuroendocrine transition. We further identified the specific neuroendocrine gene programs repressed by REST downstream of Notch in this process. Importantly, we validated the importance of REST and YAP in neuroendocrine to non-neuroendocrine cell fate switches in both developmental and tissue repair processes in the lungs. Altogether, these experiments identify conserved roles for REST and YAP in Notch-driven inhibition of the neuroendocrine cell fate in embryonic lungs, adult lungs, and lung cancer.

Scalable deep learning algorithm to compute percent pulmonary contusion among patients with rib fractures.

BACKGROUND: Pulmonary contusion exists along a spectrum of severity, yet is commonly binarily classified as present or absent. We aimed to develop a deep learning algorithm to automate percent pulmonary contusion computation and exemplify how transfer learning could facilitate large-scale validation. We hypothesized that our deep learning algorithm could automate percent pulmonary contusion computation and that greater percent contusion would be associated with higher odds of adverse inpatient outcomes among patients with rib fractures. METHODS: We evaluated admission-day chest computed tomography scans of adults 18 years or older admitted to our institution with multiple rib fractures and pulmonary contusions (2010-2020). We adapted a pretrained convolutional neural network that segments three-dimensional lung volumes and segmented contused lung parenchyma, pulmonary blood vessels, and computed percent pulmonary contusion. Exploratory analysis evaluated associations between percent pulmonary contusion (quartiles) and odds of mechanical ventilation, mortality, and prolonged hospital length of stay using multivariable logistic regression. Sensitivity analysis included pulmonary blood vessel volumes during percent contusion computation. RESULTS: A total of 332 patients met inclusion criteria (median, 5 rib fractures), among whom 28% underwent mechanical ventilation and 6% died. The study population's median (interquartile range) percent pulmonary contusion was 4% (2%-8%). Compared to the lowest quartile of percent pulmonary contusion, each increasing quartile was associated with higher adjusted odds of undergoing mechanical ventilation (odds ratio [OR], 1.5; 95% confidence interval [95% CI], 1.1-2.1) and prolonged hospitalization (OR, 1.6; 95% CI, 1.1-2.2), but not with mortality (OR, 1.1; 95% CI, 0.6-2.0). Findings were similar on sensitivity analysis. CONCLUSION: We developed a scalable deep learning algorithm to automate percent pulmonary contusion calculating using chest computed tomography scans of adults admitted with rib fractures. Open code sharing and collaborative research are needed to validate our algorithm and exploratory analysis at a large scale. Transfer learning can help harness the full potential of big data and high-performing algorithms to bring precision medicine to the bedside. LEVEL OF EVIDENCE: Prognostic and epidemiological, Level III.

c-FOS drives reversible basal to squamous cell carcinoma transition.

While squamous transdifferentiation within subpopulations of adenocarcinomas represents an important drug resistance problem, its underlying mechanism remains poorly understood. Here, using surface markers of resistant basal cell carcinomas (BCCs) and patient single-cell and bulk transcriptomic data, we uncover the dynamic roadmap of basal to squamous cell carcinoma transition (BST). Experimentally induced BST identifies activator protein 1 (AP-1) family members in regulating tumor plasticity, and we show that c-FOS plays a central role in BST by regulating the accessibility of distinct AP-1 regulatory elements. Remarkably, despite prominent changes in cell morphology and BST marker expression, we show using inducible model systems that c-FOS-mediated BST demonstrates reversibility. Blocking EGFR pathway activation after c-FOS induction partially reverts BST in vitro and prevents BST features in both mouse models and human tumors. Thus, by identifying the molecular basis of BST, our work reveals a therapeutic opportunity targeting plasticity as a mechanism of tumor resistance.

A Prediction Tool for the Presence of Ceftriaxone-Resistant Uropathogens upon Hospital Admission.

Antimicrobial resistance among uropathogens is a particularly pressing problem in the Asia-Pacific region. The objectives of this study were to determine the incidence and susceptibility of uropathogens upon hospital admission and to develop a risk-scoring model to predict the presence of ceftriaxone-resistance uropathogens (CrP). This was a retrospective observational cohort study of patients with a positive urine culture within 48 h of presentation at National University Hospital, Singapore between June 2015 and August 2015. Escherichia coli was the most common uropathogen isolated (51.7%), followed by Klebsiella pneumonia (15.1%) and Pseudomonas aeruginosa (8.2%). Overall, 372 out of 869 isolates (42.8%) were resistant to ceftriaxone. Hospitalization for ≥2 days within past 30 days, antibiotic use within the past 3 months and male gender were associated with the presence of CrP. A risk score based on these parameters successfully predicted CrP with an area under the curve of 0.68. The risk score will help clinicians to accurately predict antibiotic resistance at the individual patient level and allow physicians to safely prescribe empiric ceftriaxone in patients at low risk of CrP, thus reducing the antibiotic selection pressure that is driving carbapenem resistance in hospitals throughout Asia.