ofeng Xia

A Screen for Morphological Complexity Identifies Regulators of Switch-like Transitions between Discrete Cell Shapes

The way in which cells adopt different morphologies is not fully understood. Cell shape could be a continuous variable or restricted to a set of discrete forms. We developed quantitative methods to describe cell shape and show that Drosophila haemocytes in culture are a heterogeneous mixture of five discrete morphologies. In an RNAi screen of genes affecting the morphological complexity of heterogeneous cell populations, we found that most genes regulate the transition between discrete shapes rather than generating new morphologies. In particular, we identified a subset of genes, including the tumour suppressor PTEN, that decrease the heterogeneity of the population, leading to populations enriched in rounded or elongated forms. We show that these genes have a highly conserved function as regulators of cell shape in both mouse and human metastatic melanoma cells.

DrugComboRanker: drug combination discovery based on target network analysis

Motivation: Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients’ outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities. Results: In this study, we propose a novel systematic computational tool DrugComboRanker to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients’ genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations. Availability and implementation: The program is available on request. Contact: stwong@tmhs.org

Image-Based Chemical Screening Identifies Drug Efflux Inhibitors In Lung Cancer Cells

Cancer cells with active drug efflux capability are multidrug resistant and pose a significant obstacle for the efficacy of chemotherapy. Moreover, recent evidence suggests that high drug efflux cancer cells (HDECC) may be selectively enriched with stem-like cancer cells, which are believed to be the cause for tumor initiation and recurrence. There is a great need for therapeutic reagents that are capable of eliminating HDECCs. We developed an image-based high-content screening (HCS) system to specifically identify and analyze the HDECC population in lung cancer cells. Using the system, we screened 1,280 pharmacologically active compounds that identified 12 potent HDECC inhibitors. It is shown that these inhibitors are able to overcome multidrug resistance (MDR) and sensitize HDECCs to chemotherapeutic drugs, or directly reduce the tumorigenicity of lung cancer cells possibly by affecting stem-like cancer cells. The HCS system we established provides a new approach for identifying therapeutic reagents overcoming MDR. The compounds identified by the screening may potentially be used as potential adjuvant to improve the efficacy of chemotherapeutic drugs.

Concise Review: A High-Content Screening Approach to Stem Cell Research and Drug Discovery

High‐throughput screening (HTS) is a technology widely used for early stages of drug discovery in pharmaceutical and biotechnology industries. Recent hardware and software improvements have enabled HTS to be used in combination with subcellular resolution microscopy, resulting in cell image‐based HTS, called high‐content screening (HCS). HCS allows the acquisition of deeper knowledge at a single‐cell level such that more complex biological systems can be studied in a high‐throughput manner. The technique is particularly well‐suited for stem cell research and drug discovery, which almost inevitably require single‐cell resolutions for the detection of rare phenotypes in heterogeneous cultures. With growing availability of facilities, instruments, and reagent libraries, small‐to‐moderate scale HCS can now be carried out in regular academic labs. We envision that the HCS technique will play an increasing role in both basic mechanism study and early‐stage drug discovery on stem cells. Here, we review the development of HCS technique and its past application on stem cells and discuss possible future developments. Stem Cells2012;30:1800–1807

Identification of Small Molecule Inhibitors of Neurite Loss Induced by Aβ peptide using High Content Screening

Background: Amyloid-β-induced degeneration of neurites is a key event in Alzheimer disease. Results: We describe NeuriteIQ high content screening platform for analysis of neurite degeneration. Conclusion: We identified multiple cyclooxygenase inhibitors and agonists of PPARγ as suppressors of Aβ-induced neurite loss. Significance: Our study demonstrates the feasibility of using NeuriteQ to discover inhibitors of neurite loss and provide a new insight into neurite degeneration. Multiple lines of evidence indicate a strong relationship between Αβ peptide-induced neurite degeneration and the progressive loss of cognitive functions in Alzheimer disease (AD) patients and in AD animal models. This prompted us to develop a high content screening assay (HCS) and Neurite Image Quantitator (NeuriteIQ) software to quantify the loss of neuronal projections induced by Aβ peptide neurons and enable us to identify new classes of neurite-protective small molecules, which may represent new leads for AD drug discovery. We identified thirty-six inhibitors of Aβ-induced neurite loss in the 1,040-compound National Institute of Neurological Disorders and Stroke (NINDS) custom collection of known bioactives and FDA approved drugs. Activity clustering showed that non-steroidal anti-inflammatory drugs (NSAIDs) were significantly enriched among the hits. Notably, NSAIDs have previously attracted significant attention as potential drugs for AD; however their mechanism of action remains controversial. Our data revealed that cyclooxygenase-2 (COX-2) expression was increased following Aβ treatment. Furthermore, multiple distinct classes of COX inhibitors efficiently blocked neurite loss in primary neurons, suggesting that increased COX activity contributes to Aβ peptide-induced neurite loss. Finally, we discovered that the detrimental effect of COX activity on neurite integrity may be mediated through the inhibition of peroxisome proliferator-activated receptor γ (PPARγ) activity. Overall, our work establishes the feasibility of identifying small molecule inhibitors of Aβ-induced neurite loss using the NeuriteIQ pipeline and provides novel insights into the mechanisms of neuroprotection by NSAIDs.

Genome-Wide RNAi Screening Identifies Genes Inhibiting the Migration of Glioblastoma Cells

Glioblastoma Multiforme (GBM) cells are highly invasive, infiltrating into the surrounding normal brain tissue, making it impossible to completely eradicate GBM tumors by surgery or radiation. Increasing evidence also shows that these migratory cells are highly resistant to cytotoxic reagents, but decreasing their migratory capability can re-sensitize them to chemotherapy. These evidences suggest that the migratory cell population may serve as a better therapeutic target for more effective treatment of GBM. In order to understand the regulatory mechanism underlying the motile phenotype, we carried out a genome-wide RNAi screen for genes inhibiting the migration of GBM cells. The screening identified a total of twenty-five primary hits; seven of them were confirmed by secondary screening. Further study showed that three of the genes, FLNA, KHSRP and HCFC1, also functioned in vivo, and knocking them down caused multifocal tumor in a mouse model. Interestingly, two genes, KHSRP and HCFC1, were also found to be correlated with the clinical outcome of GBM patients. These two genes have not been previously associated with cell migration.

A 3D multiscale model of cancer stem cell in tumor development.

BackgroundRecent reports indicate that a subgroup of tumor cells named cancer stem cells (CSCs) or tumor initiating cells (TICs) are responsible for tumor initiation, growth and drug resistance. This subgroup of tumor cells has self-renewal capacity and could differentiate into heterogeneous tumor cell populations through asymmetric proliferation. The idea of CSC provides informative insights into tumor initiation, metastasis and treatment. However, the underlying mechanisms of CSCs regulating tumor behaviors are unclear due to the complex cancer system. To study the functions of CSCs in the complex tumor system, a few mathematical modeling studies have been proposed. Whereas, the effect of microenvironment (mE) factors, the behaviors of CSCs, progenitor tumor cells (PCs) and differentiated tumor cells (TCs), and the impact of CSC fraction and signaling heterogeneity, are not adequately explored yet.MethodsIn this study, a novel 3D multi-scale mathematical modeling is proposed to investigate the behaviors of CSCsin tumor progressions. The model integrates CSCs, PCs, and TCs together with a few essential mE factors. With this model, we simulated and investigated the tumor development and drug response under different CSC content and heterogeneity.ResultsThe simulation results shown that the fraction of CSCs plays a critical role in driving the tumor progression and drug resistance. It is also showed that the pure chemo-drug treatment was not a successful treatment, as it resulted in a significant increase of the CSC fraction. It further shown that the self-renew heterogeneity of the initial CSC population is a cause of the heterogeneity of the derived tumors in terms of the CSC fraction and response to drug treatments.ConclusionsThe proposed 3D multi-scale model provides a new tool for investigating the behaviors of CSC in CSC-initiated tumors, which enables scientists to investigate and generate testable hypotheses about CSCs in tumor development and drug response under different microenvironments and drug perturbations.

Integrating multi-scale blob/curvilinear detector techniques and multi-level sets for automated segmentation of stem cell images

Studies of differentiation abilities of stem cells have been attracting a lot of attention over the last years. Microscopy can be used to record details of the differentiation process of stem cells under different perturbations and is an important tool for studying stem cell differentiation. Since it is infeasible to quantitatively analyze a huge amount of image data manually, automated image analysis systems are urgently needed. However, the complicated morphological appearances of stem cells are challenging to the existing segmentation methods. Herein, we propose a new, automated scheme for stem cell segmentation. This scheme first uses the multi-scale blob and curvilinear structure detectors to delineate the skeletons of stem cells quickly and then segment out stem cells by refining the skeletons to the cell boundaries using multi-level sets. The initial experimental results indicate the effectiveness of the proposed scheme.

A High-content screen identifies compounds promoting the neuronal differentiation and the midbrain dopamine neuron specification of human neural progenitor cells.

Small molecule compounds promoting the neuronal differentiation of stem/progenitor cells are of pivotal importance to regenerative medicine. We carried out a high-content screen to systematically characterize known bioactive compounds, on their effects on the neuronal differentiation and the midbrain dopamine (mDA) neuron specification of neural progenitor cells (NPCs) derived from the ventral mesencephalon of human fetal brain. Among the promoting compounds three major pharmacological classes were identified including the statins, TGF-βRI inhibitors, and GSK-3 inhibitors. The function of each class was also shown to be distinct, either to promote both the neuronal differentiation and mDA neuron specification, or selectively the latter, or promote the former but suppress the latter. We then carried out initial investigation on the possible mechanisms underlying, and demonstrated their applications on NPCs derived from human pluripotent stem cells (PSCs). Our study revealed the potential of several small molecule compounds for use in the directed differentiation of human NPCs. The screening result also provided insight into the signaling network regulating the differentiation of human NPCs.

Differential diagnosis of lung carcinoma with three-dimensional quantitative molecular vibrational imaging

The advent of molecularly targeted therapies requires effective identification of the various cell types of non-small cell lung carcinomas (NSCLC). Currently, cell type diagnosis is performed using small biopsies or cytology specimens that are often insufficient for molecular testing after morphologic analysis. Thus, the ability to rapidly recognize different cancer cell types, with minimal tissue consumption, would accelerate diagnosis and preserve tissue samples for subsequent molecular testing in targeted therapy. We report a label-free molecular vibrational imaging framework enabling three-dimensional (3-D) image acquisition and quantitative analysis of cellular structures for identification of NSCLC cell types. This diagnostic imaging system employs superpixel-based 3-D nuclear segmentation for extracting such disease-related features as nuclear shape, volume, and cell-cell distance. These features are used to characterize cancer cell types using machine learning. Using fresh unstained tissue samples derived from cell lines grown in a mouse model, the platform showed greater than 97% accuracy for diagnosis of NSCLC cell types within a few minutes. As an adjunct to subsequent histology tests, our novel system would allow fast delineation of cancer cell types with minimum tissue consumption, potentially facilitating on-the-spot diagnosis, while preserving specimens for additional tests. Furthermore, 3-D measurements of cellular structure permit evaluation closer to the native state of cells, creating an alternative to traditional 2-D histology specimen evaluation, potentially increasing accuracy in diagnosing cell type of lung carcinomas.