Lipid Metabolic Reprogramming Extends beyond Histologic Tumor Demarcations in Operable Human Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest malignancies and potentially curable only with radical surgical resection at early stages. The tumor microenvironment has been shown to be central to the development and progression of PDAC. A better understanding of how early human PDAC metabolically communicates with its environment and differs from healthy pancreas could help improve PDAC diagnosis and treatment. Here we performed deep proteomic analyses from diagnostic specimens of operable, treatment-naïve PDAC patients (n = 14), isolating four tissue compartments by laser-capture microdissection: PDAC lesions, tumor-adjacent but morphologically benign exocrine glands, and connective tissues neighboring each of these compartments. Protein and pathway levels were compared between compartments and with control pancreatic proteomes. Selected targets were studied immunohistochemically in the 14 patients and in additional tumor microarrays, and lipid deposition was assessed by nonlinear label-free imaging (n = 16). Widespread downregulation of pancreatic secretory functions was observed, which was paralleled by high cholesterol biosynthetic activity without prominent lipid storage in the neoplastic cells. Stromal compartments harbored ample blood apolipoproteins, indicating abundant microvasculature at the time of tumor removal. The features best differentiating the tumor-adjacent exocrine tissue from healthy control pancreas were defined by upregulation of proteins related to lipid transport. Importantly, histologically benign exocrine regions harbored the most significant prognostic pathways, with proteins involved in lipid transport and metabolism, such as neutral cholesteryl ester hydrolase 1, associating with shorter survival. In conclusion, this study reveals prognostic molecular changes in the exocrine tissue neighboring pancreatic cancer and identifies enhanced lipid transport and metabolism as its defining features. SIGNIFICANCE: In clinically operable pancreatic cancer, regions distant from malignant cells already display proteomic changes related to lipid transport and metabolism that affect prognosis and may be pharmacologically targeted.

Regression plane concept for analysing continuous cellular processes with machine learning.

Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool enabling class-free phenotypic supervised machine learning, to describe and explore biological data in a continuous manner. First, we compare traditional classification with regression in a simulated experimental setup. Second, we use our framework to identify genes involved in regulating triglyceride levels in human cells. Subsequently, we analyse a time-lapse dataset on mitosis to demonstrate that the proposed methodology is capable of modelling complex processes at infinite resolution. Finally, we show that hemocyte differentiation in Drosophila melanogaster has continuous characteristics.

Routing Nanomolar Protein Cargoes to Lipid Raft-Mediated/Caveolar Endocytosis through a Ganglioside GM1-Specific Recognition Tag.

There is a pressing need to develop ways to deliver therapeutic macromolecules to their intracellular targets. Certain viral and bacterial proteins are readily internalized in functional form through lipid raft-mediated/caveolar endocytosis, but mimicking this process with protein cargoes at therapeutically relevant concentrations is a great challenge. Targeting ganglioside GM1 in the caveolar pits triggers endocytosis. A pentapeptide sequence WYKYW is presented, which specifically captures the glycan moiety of GM1 (K D = 24 nm). The WYKYW-tag facilitates the GM1-dependent endocytosis of proteins in which the cargo-loaded caveosomes do not fuse with lysosomes. A structurally intact immunoglobulin G complex (580 kDa) is successfully delivered into live HeLa cells at extracellular concentrations ranging from 20 to 160 nm, and escape of the cargo proteins to the cytosol is observed. The short peptidic WYKYW-tag is an advantageous endocytosis routing sequence for lipid raft-mediated/caveolar cell delivery of therapeutic macromolecules, especially for cancer cells that overexpress GM1.

nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer.

Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information.

Melanoma-Derived Exosomes Induce PD-1 Overexpression and Tumor Progression via Mesenchymal Stem Cell Oncogenic Reprogramming.

Recently, it has been described that programmed cell death protein 1 (PD-1) overexpressing melanoma cells are highly aggressive. However, until now it has not been defined which factors lead to the generation of PD-1 overexpressing subpopulations. Here, we present that melanoma-derived exosomes, conveying oncogenic molecular reprogramming, induce the formation of a melanoma-like, PD-1 overexpressing cell population (mMSCPD-1+) from naïve mesenchymal stem cells (MSCs). Exosomes and mMSCPD-1+ cells induce tumor progression and expression of oncogenic factors in vivo. Finally, we revealed a characteristic, tumorigenic signaling network combining the upregulated molecules (e.g., PD-1, MET, RAF1, BCL2, MTOR) and their upstream exosomal regulating proteins and miRNAs. Our study highlights the complexity of exosomal communication during tumor progression and contributes to the detailed understanding of metastatic processes.

Seipin Facilitates Triglyceride Flow to Lipid Droplet and Counteracts Droplet Ripening via Endoplasmic Reticulum Contact.

Seipin is an oligomeric integral endoplasmic reticulum (ER) protein involved in lipid droplet (LD) biogenesis. To study the role of seipin in LD formation, we relocalized it to the nuclear envelope and found that LDs formed at these new seipin-defined sites. The sites were characterized by uniform seipin-mediated ER-LD necks. At low seipin content, LDs only grew at seipin sites, and tiny, growth-incompetent LDs appeared in a Rab18-dependent manner. When seipin was removed from ER-LD contacts within 1 h, no lipid metabolic defects were observed, but LDs became heterogeneous in size. Studies in seipin-ablated cells and model membranes revealed that this heterogeneity arises via a biophysical ripening process, with triglycerides partitioning from smaller to larger LDs through droplet-bilayer contacts. These results suggest that seipin supports the formation of structurally uniform ER-LD contacts and facilitates the delivery of triglycerides from ER to LDs. This counteracts ripening-induced shrinkage of small LDs.

Intelligent image-based in situ single-cell isolation.

Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org.