Imaging malaria sporozoites in the dermis of the mammalian host

The initial phase of malaria infection is the pre-erythrocytic phase, which begins when parasites are injected by the mosquito into the dermis and ends when parasites are released from hepatocytes into the blood. We present here a protocol for the in vivo imaging of GFP-expressing sporozoites in the dermis of rodents, using the combination of a high-speed spinning-disk confocal microscope and a high-speed charge-coupled device (CCD) camera permitting rapid in vivo acquisitions. the steps of this protocol indicate how to infect mice through the bite of infected Anopheles stephensi mosquitoes, record the sporozoites' fate in the mouse ear and to present the data as maximum-fluorescence-intensity projections, time-lapse representations and movie clips. This protocol permits investigating the various aspects of sporozoite behavior in a quantitative manner, such as motility in the matrix, cell traversal, crossing the endothelial barrier of both blood and lymphatic vessels and intravascular gliding. Applied to genetically modified parasites and/or mice, these imaging techniques should be useful for studying the cellular and molecular bases of Plasmodium sporozoite infection in vivo.Inst Pasteur, Unite Biol & Genet Paludisme, F-75724 Paris 15, FranceWeb of Scienc

The SPI-2 type III secretion system restricts motility of Salmonella-containing vacuoles

Intracellular replication of Salmonella enterica occurs in membrane-bound compartments, called Salmonella-containing vacuoles (SCVs). Following invasion of epithelial cells, most SCVs migrate to a perinuclear region and replicate in close association with the Golgi network. The association of SCVs with the Golgi is dependent on the Salmonella-pathogenicity island-2 (SPI-2) type III secretion system (T3SS) effectors SseG, SseF and SifA. However, little is known about the dynamics of SCV movement. Here, we show that in epithelial cells, 2 h were required for migration of the majority of SCVs to within 5 μm from the microtubule organizing centre (MTOC), which is located in the same subcellular region as the Golgi network. This initial SCV migration was saltatory, bidirectional and microtubule-dependent. An intact Golgi, SseG and SPI-2 T3SS were dispensable for SCV migration to the MTOC, but were essential for maintenance of SCVs in that region. Live-cell imaging between 4 and 8 h post invasion revealed that the majority of wild-type SCVs displaced less than 2 μm in 20 min from their initial starting positions. In contrast, between 6 and 8 h post invasion the majority of vacuoles containing sseG, sseF or ssaV mutant bacteria displaced more than 2 μm in 20 min from their initial starting positions, with some undergoing large and dramatic movements. Further analysis of the movement of SCVs revealed that large displacements were a result of increased SCV speed rather than a change in their directionality, and that SseG influences SCV motility by restricting vacuole speed within the MTOC/Golgi region. SseG might function by tethering SCVs to Golgi-associated molecules, or by controlling microtubule motors, for example by inhibiting kinesin recruitment or promoting dynein recruitment

Telomeric Heterochromatin Propagation and Histone Acetylation Control Mutually Exclusive Expression of Antigenic Variation Genes in Malaria Parasites

SummaryMalaria parasites use antigenic variation to avoid immune clearance and increase the duration of infection in the human host. Variation at the surface of P. falciparum-infected erythrocytes is mediated by the differential control of a family of surface antigens encoded by var genes. Switching of var gene expression occurs in situ, mostly from telomere-associated loci, without detectable DNA alterations, suggesting that it is controlled by chromatin structure. We have identified chromatin modifications at telomeres that spread far into telomere-proximal regions, including var gene loci (>50 kb). One type of modification is mediated by a protein homologous to yeast Sir2 called PfSir2, which forms a chromosomal gradient of heterochromatin structure and histone hypoacetylation. Upon activation of a specific telomere-associated var gene, PfSir2 is removed from the promoter region and acetylation of histone occurs. Our data demonstrate that mutually exclusive transcription of var genes is linked to the dynamic remodeling of chromatin

Imaging of Red-Shifted Light From Bioluminescent Tumors Using Fluorescence by Unbound Excitation From Luminescence

Early detection of tumors is today a major challenge and requires sensitive imaging methodologies coupled with new efficient probes. In vivo optical bioluminescence imaging has been widely used in the field of preclinical oncology to visualize tumors and several cancer cell lines have been genetically modified to provide bioluminescence signals. However, the light emitted by the majority of commonly used luciferases is usually in the blue part of the visible spectrum, where tissue absorption is still very high, making deep tissue imaging non-optimal, and calling for optimized optical imaging methodologies. We have previously shown that red-shifting of bioluminescence signal by Fluorescence Unbound Excitation from Luminescence (FUEL) is a mean to increase bioluminescence signal sensitivity detection in vivo. Here, we applied FUEL to tumor detection in two different subcutaneous tumor models: the auto-luminescent human embryonic kidney (HEK293) cell line and the murine B16-F10 melanoma cell line previously transfected with a plasmid encoding the Luc2 firefly luciferase. Tumor size and bioluminescence were measured over time and tumor vascularization characterized. We then locally injected near infrared emitting Quantum Dots (NIR QDs) in the tumor site and observed a red-shifting of bioluminescence signal by (FUEL) indicating that FUEL could be used to allow deeper tumor detection in mice

Objective comparison of particle tracking methods

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers

Standardized Whole-Blood Transcriptional Profiling Enables the Deconvolution of Complex Induced Immune Responses

SummarySystems approaches for the study of immune signaling pathways have been traditionally based on purified cells or cultured lines. However, in vivo responses involve the coordinated action of multiple cell types, which interact to establish an inflammatory microenvironment. We employed standardized whole-blood stimulation systems to test the hypothesis that responses to Toll-like receptor ligands or whole microbes can be defined by the transcriptional signatures of key cytokines. We found 44 genes, identified using Support Vector Machine learning, that captured the diversity of complex innate immune responses with improved segregation between distinct stimuli. Furthermore, we used donor variability to identify shared inter-cellular pathways and trace cytokine loops involved in gene expression. This provides strategies for dimension reduction of large datasets and deconvolution of innate immune responses applicable for characterizing immunomodulatory molecules. Moreover, we provide an interactive R-Shiny application with healthy donor reference values for induced inflammatory genes

Screening out irrelevant cell-based models of disease

The common and persistent failures to translate promising preclinical drug candidates into clinical success highlight the limited effectiveness of disease models currently used in drug discovery. An apparent reluctance to explore and adopt alternative cell-and tissue-based model systems, coupled with a detachment from clinical practice during assay validation, contributes to ineffective translational research. To help address these issues and stimulate debate, here we propose a set of principles to facilitate the definition and development of disease-relevant assays, and we discuss new opportunities for exploiting the latest advances in cell-based assay technologies in drug discovery, including induced pluripotent stem cells, three-dimensional (3D) co-culture and organ-on-a-chip systems, complemented by advances in single-cell imaging and gene editing technologies. Funding to support precompetitive, multidisciplinary collaborations to develop novel preclinical models and cell-based screening technologies could have a key role in improving their clinical relevance, and ultimately increase clinical success rates

Deep learning in image-based phenotypic drug discovery

International audienceModern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal ‘hit’ compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD)