In Drosophila melanogaster, widely used mitotic recombination-based strategies generate mosaic flies with positive readout for only one daughter cell after division. To differentially label both ...daughter cells, we developed the twin spot generator (TSG) technique, which through mitotic recombination generates green and red twin spots that are detectable after the first cell division as single cells. We propose wide applications of TSG to lineage and genetic mosaic studies.
Lab-on-Chip electrochemical sensors, such as Ion-Sensitive Field-Effect Transistors (ISFETs), are being developed for use in point-of-care diagnostics, such as pH detection of tumour ...microenvironments, due to their integration with standard Complementary Metal Oxide Semiconductor (CMOS) technology. With this approach, the passivation of the CMOS process is used as a sensing layer to minimise post-processing, and Silicon Nitride (Si3N4) is the most common material at the microchip surface. ISFETs have the potential to be used for cell-based assays however, there is a poor understanding of the biocompatibility of microchip surfaces. Here, we quantitatively evaluated cell adhesion, morphogenesis, proliferation and mechano-responsiveness of both normal and cancer cells cultured on a Si3N4, sensor surface. We demonstrate that both normal and cancer cell adhesion decreased on Si3N4. Activation of the mechano-responsive transcription regulators, YAP/TAZ, are significantly decreased in cancer cells on Si3N4 in comparison to standard cell culture plastic, whilst proliferation marker, Ki67, expression markedly increased. Non-tumorigenic cells on chip showed less sensitivity to culture on Si3N4 than cancer cells. Treatment with extracellular matrix components increased cell adhesion in normal and cancer cell cultures, surpassing the adhesiveness of plastic alone. Moreover, poly-l-ornithine and laminin treatment restored YAP/TAZ levels in both non-tumorigenic and cancer cells to levels comparable to those observed on plastic. Thus, engineering the electrochemical sensor surface with treatments will provide a more physiologically relevant environment for future cell-based assay development on chip.
Abstract
Cell shapes have long served as hallmarks for cancer diagnostics and therapeutics evaluations. This work converges public available image and transcriptome profiles to build a ...drug-gene-shape networks, thus facilitate the usage of cell shape features as drug targets during the development of novel cancer therapeutics. We took advantage of a previous work (Sailem et. al., 2017) of image-omits modeling where 504 shape-correlated genes were identified for 20 cell shape features across 18 breast cancer cell lines. Transcriptome profiles from Broad Institute Clue.io database were used to identify small molecular compounds that can significantly impact those shape-correlated genes. As a result, 22 compound families of at least two members were identified as candidates that can significantly impact the shape-correlating gene sets for 13 shape features. Our work re-opens the doors for using cell shape as drug target and considering the impact on cell shapes during drug repurposing and design of combination therapies for breast cancer.
Compound families with the most significant correlations with gene sets regulating average cell areaCompound famileAverage scoremTOR inhibitors94.975Tubulin inhibitors94.615Adrenergic receptor antagonist94.545PKC inhibitor-93.580MEK inhibitor-94.137HDAC inhibitor-94.153
Citation Format: Zheng Yin, Chris Bakal, Stephen T. Wong. Predicting drug effects on breast cancer cell shapes by converging image and transcriptomic profiles abstract. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6570.
The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular ...phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens.
Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos Additional files 1, 2, dataset for cell cycle phase identification using HeLa cells Additional files 1, 3, 4 and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%-90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms.
We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
During metastasis, tumour cells navigating the vascular circulatory system-circulating tumour cells (CTCs)-encounter capillary beds, where they start the process of extravasation. Biomechanical ...constriction forces exerted by the microcirculation compromise the survival of tumour cells within capillaries, but a proportion of CTCs manage to successfully extravasate and colonise distant sites. Despite the profound importance of this step in the progression of metastatic cancers, the factors about this deadly minority of cells remain elusive. Growing evidence suggests that mechanical forces exerted by the capillaries might induce adaptive mechanisms in CTCs, enhancing their survival and metastatic potency. Advances in microfluidics have enabled a better understanding of the cell-survival capabilities adopted in capillary-mimicking constrictions. In this review, we will highlight adaptations developed by CTCs to endure mechanical constraints in the microvasculature and outline how these mechanical forces might trigger dynamic changes towards a more invasive phenotype. A better understanding of the dynamic mechanisms adopted by CTCs within the microcirculation that ultimately lead to metastasis could open up novel therapeutic avenues.
The associations between clinical phenotypes (tumor grade, survival) and cell phenotypes, such as shape, signaling activity, and gene expression, are the basis for cancer pathology, but the ...mechanisms explaining these relationships are not always clear. The generation of large data sets containing information regarding cell phenotypes and clinical data provides an opportunity to describe these mechanisms. Here, we develop an image-omics approach to integrate quantitative cell imaging data, gene expression, and protein-protein interaction data to systematically describe a "shape-gene network" that couples specific aspects of breast cancer cell shape to signaling and transcriptional events. The actions of this network converge on NF-κB, and support the idea that NF-κB is responsive to mechanical stimuli. By integrating RNAi screening data, we identify components of the shape-gene network that regulate NF-κB in response to cell shape changes. This network was also used to generate metagene models that predict NF-κB activity and aspects of morphology such as cell area, elongation, and protrusiveness. Critically, these metagenes also have predictive value regarding tumor grade and patient outcomes. Taken together, these data strongly suggest that changes in cell shape, driven by gene expression and/or mechanical forces, can promote breast cancer progression by modulating NF-κB activation. Our findings highlight the importance of integrating phenotypic data at the molecular level (signaling and gene expression) with those at the cellular and tissue levels to better understand breast cancer oncogenesis.
The morphology of breast cancer cells is often used as an indicator of tumor severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within ...a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastasize. However, the exact regulatory systems that define these changes remain poorly characterized. In this study, we used a network-systems approach to integrate imaging data and RNA-seq expression data. Our workflow allowed the discovery of unbiased and context-specific gene expression signatures and cell signaling subnetworks relevant to the regulation of cell shape, rather than focusing on the identification of previously known, but not always representative, pathways. By constructing a cell-shape signaling network from shape-correlated gene expression modules and their upstream regulators, we found central roles for developmental pathways such as WNT and Notch, as well as evidence for the fine control of NF-kB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates a gene expression module enriched in the RAP1 signaling pathway as a mediator between the sensing of mechanical stimuli and regulation of NF-kB activity, with specific relevance to cell shape in breast cancer.
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