Specific Biological Questions Research Articles

PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3′-end sequencing

BackgroundOver the last decade the drop in short-read sequencing costs has allowed experimental techniques utilizing sequencing to address specific biological questions to proliferate, oftentimes outpacing standardized or effective analysis approaches for the data generated. There are growing amounts of bacterial 3′-end sequencing data, yet there is currently no commonly accepted analysis methodology for this datatype. Most data analysis approaches are somewhat ad hoc and, despite the presence of substantial signal within annotated genes, focus on genomic regions outside the annotated genes (e.g. 3′ or 5′ UTRs). Furthermore, the lack of consistent systematic analysis approaches, as well as the absence of genome-wide ground truth data, make it impossible to compare conclusions generated by different labs, using different organisms.ResultsWe present PIPETS, (Poisson Identification of PEaks from Term-Seq data), an R package available on Bioconductor that provides a novel analysis method for 3'-end sequencing data. PIPETS is a statistically informed, gene-annotation agnostic methodology. Across two different datasets from two different organisms, PIPETS identified significant 3'-end termination signal across a wider range of annotated genomic contexts than existing analysis approaches, suggesting that existing approaches may miss biologically relevant signal. Furthermore, assessment of the previously called 3′-end positions not captured by PIPETS showed that they were uniformly very low coverage.ConclusionsPIPETS provides a broadly applicable platform to explore and analyze 3′-end sequencing data sets from across different organisms. It requires only the 3′-end sequencing data, and is broadly accessible to non-expert users.

PIPETS: A statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3'-end sequencing.

Over the last decade the drop in short-read sequencing costs has allowed experimental techniques utilizing sequencing to address specific biological questions to proliferate, oftentimes outpacing standardized or effiective analysis approaches for the data generated. There are growing amounts of bacterial 3'-end sequencing data, yet there is currently no commonly accepted analysis methodology for this datatype. Most data analysis approaches are somewhat ad hoc and, despite the presence of substantial signal within annotated genes, focus on genomic regions outside the annotated genes (e.g. 3' or 5' UTRs). Furthermore, the lack of consistent systematic analysis approaches, as well as the absence of genome-wide ground truth data, make it impossible to compare conclusions generated by diffierent labs, using diffierent organisms. We present PIPETS, ( P oisson I dentification of PE aks from T erm- S eq data), an R package available on Bioconductor that provides a novel analysis method for 3'-end sequencing data. PIPETS is a statistically informed, gene-annotation agnostic methodology. Across two diffierent datasets from two diffierent organisms, PIPETS identified significant 3'-end termination signal across a wider range of annotated genomic contexts than existing analysis approaches, suggesting that existing approaches may miss biologically relevant signal. Furthermore, assessment of the previously called 3'-end positions not captured by PIPETS showed that they were uniformly very low coverage. PIPETS provides a broadly applicable platform to explore and analyze 3'-end sequencing data sets from across diffierent organisms. It requires only the 3'-end sequencing data, and is broadly accessible to non-expert users.

Spatial transcriptomics techniques and its applications in plant research

The heterogeneity of gene expression in plant cells plays a crucial role in determining the functional differences among tissues. Recent advancements in spatial transcriptome (ST) technology have significantly contributed to the study of specific biological questions in plants. This technology has been successfully applied to examine cell development, identification, and stress resistance. This review aims to explore the application of ST technology in plants by reviewing three aspects: the development of ST technology, its current application in plants, and future research directions. The review provides a systematic description of the development process of ST technology, with a focus on analyzing its progress in studying plant cell growth and differentiation, plant cell identification, and stress resistance. In addition, the challenges faced by ST technology in plant applications are summarized, along with proposed future directions for plant research, including the advantages of combining other omics technologies with ST technology to tackle scientific challenges in the field of plants.

How to apply the broad toolbox of correlative light and electron microscopy to address a specific biological question.

Correlative light and electron microscopy (CLEM) is an approach that combines the strength of multiple imaging techniques to obtain complementary information about a given specimen. The "toolbox" for CLEM is broad, making it sometimes difficult to choose an appropriate approach for a given biological question. In this chapter, we provide experimental details for three CLEM approaches that can help the interested reader in designing a personalized CLEM strategy for obtaining ultrastructural data by using transmission electron microscopy (TEM). First, we describe chemical fixation of cells grown on a solid support (broadest approach). Second, we apply high-pressure freezing/freeze substitution to describe cellular ultrastructure (cryo-immobilization approach). Third, we give a protocol for a ultrastructural labeling by immuno-electron microscopy (immuno-EM approach). In addition, we also describe how to overlay fluorescence and electron microscopy images, an approach that is applicable to each of the reported different CLEM strategies. Here we provide step-by step descriptions prior to discussing possible technical problems and variations of these three general schemes to suit different models or different biological questions. This chapter is written for electron microscopists that are new to CLEM and unsure how to begin. Therefore, our protocols are meant to provide basic information with further references that should help the reader get started with applying a tailored strategy for a specific CLEM experiment.

The monoicous secondarily aquatic liverwort Ricciocarpos natans as a model within the radiation of derived Marchantiopsida

Liverworts represent one of six embryophyte lineages that have a Devonian, or earlier, origin, and are, at present, represented by only Marchantia polymorpha as an established model. Ricciocarpos natans is a secondarily monoicous aquatic liverwort with a worldwide distribution, being found on all continents except Antarctica. Ricciocarpos, a monotypic genus, forms a sister relationship with Riccia, the largest genus of the Marchantiopsida (~250 species), diverging from their common ancestor in the mid-Cretaceous. R. natans is typically found on small stagnant ponds and billabongs (seasonal pools), where it assumes a typical ‘aquatic’ form with long scale keels for stabilization on the water surface. But, as water bodies dry, plants may become stranded and subsequently shift their development to assume a ‘terrestrial’ form with rhizoids anchoring the plants to the substrate. We developed R. natans as a model to address a specific biological question — what are the genomic consequences when monoicy evolves from ancestral dioicy where sex is chromosomally determined? However, R. natans possesses other attributes that makes it a model to investigate a variety of biological processes. For example, it provides a foundation to explore the evolution of sexual systems within Riccia, where it appears monoicy may have evolved many times independently. Furthermore, the worldwide distribution of R. natans postdates plate tectonic driven continent separation, and thus, provides an intriguing model for population genomics. Finally, the transition from an aquatic growth form to a terrestrial growth form is mediated by the phytohormone abscisic acid, and represents convergent evolution with a number of other aquatic embryophytes, a concept we explore further here.

AMEND: active module identification using experimental data and network diffusion

BackgroundMolecular interaction networks have become an important tool in providing context to the results of various omics experiments. For example, by integrating transcriptomic data and protein–protein interaction (PPI) networks, one can better understand how the altered expression of several genes are related with one another. The challenge then becomes how to determine, in the context of the interaction network, the subset(s) of genes that best captures the main mechanisms underlying the experimental conditions. Different algorithms have been developed to address this challenge, each with specific biological questions in mind. One emerging area of interest is to determine which genes are equivalently or inversely changed between different experiments. The equivalent change index (ECI) is a recently proposed metric that measures the extent to which a gene is equivalently or inversely regulated between two experiments. The goal of this work is to develop an algorithm that makes use of the ECI and powerful network analysis techniques to identify a connected subset of genes that are highly relevant to the experimental conditions.ResultsTo address the above goal, we developed a method called Active Module identification using Experimental data and Network Diffusion (AMEND). The AMEND algorithm is designed to find a subset of connected genes in a PPI network that have large experimental values. It makes use of random walk with restart to create gene weights, and a heuristic solution to the Maximum-weight Connected Subgraph problem using these weights. This is performed iteratively until an optimal subnetwork (i.e., active module) is found. AMEND was compared to two current methods, NetCore and DOMINO, using two gene expression datasets.ConclusionThe AMEND algorithm is an effective, fast, and easy-to-use method for identifying network-based active modules. It returned connected subnetworks with the largest median ECI by magnitude, capturing distinct but related functional groups of genes. Code is freely available at https://github.com/samboyd0/AMEND.

Proteome Mapping of the Human Pancreatic Islet Microenvironment Reveals Endocrine–Exocrine Signaling Sphere of Influence

The need for a clinically accessible method with the ability to match protein activity within heterogeneous tissues is currently unmet by existing technologies. Our proteomics sample preparation platform, named microPOTS (Microdroplet Processing in One pot for Trace Samples), can be used to measure relative protein abundance in micron-scale samples alongside the spatial location of each measurement, thereby tying biologically interesting proteins and pathways to distinct regions. However, given the smaller pixel/voxel number and amount of tissue measured, standard mass spectrometric analysis pipelines have proven inadequate. Here we describe how existing computational approaches can be adapted to focus on the specific biological questions asked in spatial proteomics experiments. We apply this approach to present an unbiased characterization of the human islet microenvironment comprising the entire complex array of cell types involved while maintaining spatial information and the degree of the islet’s sphere of influence. We identify specific functional activity unique to the pancreatic islet cells and demonstrate how far their signature can be detected in the adjacent tissue. Our results show that we can distinguish pancreatic islet cells from the neighboring exocrine tissue environment, recapitulate known biological functions of islet cells, and identify a spatial gradient in the expression of RNA processing proteins within the islet microenvironment.

CO-CLUSTERING OF SPATIALLY RESOLVED TRANSCRIPTOMIC DATA.

Spatial transcriptomics is a groundbreaking technology that allows the measurement of the activity of thousands of genes in a tissue sample and maps where the activity occurs. This technology has enabled the study of the spatial variation of the genes across the tissue. Comprehending gene functions and interactions in different areas of the tissue is of great scientific interest, as it might lead to a deeper understanding of several key biological mechanisms, such as cell-cell communication or tumor-microenvironment interaction. To do so, one can group cells of the same type and genes that exhibit similar expression patterns. However, adequate statistical tools that exploit the previously unavailable spatial information to more coherently group cells and genes are still lacking. In this work, we introduce SpaRTaCo, a new statistical model that clusters the spatial expression profiles of the genes according to a partition of the tissue. This is accomplished by performing a co-clustering, i.e., inferring the latent block structure of the data and inducing two types of clustering: of the genes, using their expression across the tissue, and of the image areas, using the gene expression in the spots where the RNA is collected. Our proposed methodology is validated with a series of simulation experiments and its usefulness in responding to specific biological questions is illustrated with an application to a human brain tissue sample processed with the 10X-Visium protocol.

Resolving Deleterious and Near-Neutral Effects Requires Different Pooled Fitness Assay Designs.

Pooled sequencing-based fitness assays are a powerful and widely used approach to quantifying fitness of thousands of genetic variants in parallel. Despite the throughput of such assays, they are prone to biases in fitness estimates, and errors in measurements are typically larger for deleterious fitness effects, relative to neutral effects. In practice, designing pooled fitness assays involves tradeoffs between the number of timepoints, the sequencing depth, and other parameters to gain as much information as possible within a feasible experiment. Here, we combined simulations and reanalysis of an existing experimental dataset to explore how assay parameters impact measurements of near-neutral and deleterious fitness effects using a standard fitness estimator. We found that sequencing multiple timepoints at relatively modest depth improved estimates of near-neutral fitness effects, but systematically biased measurements of deleterious effects. We showed that a fixed total number of reads, deeper sequencing at fewer timepoints improved resolution of deleterious fitness effects. Our results highlight a tradeoff between measurement of deleterious and near-neutral effect sizes for a fixed amount of data and suggest that fitness assay design should be tuned for fitness effects that are relevant to the specific biological question.

Design and Validation of Ultrahigh-plex Discovery Panels for Immune Checkpoint Targets

Abstract Ultrahigh-plex single-cell spatial phenotyping is revolutionizing immuno-oncology research. To enable in-depth characterization of the tumor microenvironment (TME), we have developed and validated pre-optimized antibody panels on cancer tissues to reveal key cell types and cellular architecture using the PhenoCycler™-Fusion system. We designed multiple panels that answer specific biological questions about the TME, and once multiplexed together create a comprehensive view of the structure and function of the TME. The resulting PhenoCode™ Discovery panels include markers that identify immune cell types, activation states, immune checkpoints, and mediators of proliferation. Each antibody in the panel was conjugated to an oligo barcode and stained on human FFPE tissue. Staining specificity of each conjugated antibody was assessed by comparing immunofluorescent results from PhenoCycler™ with a DAB chromogenic immunohistochemistry assay. The slide stained with a PhenoCode™ Discovery panel was imaged on a PhenoCycler™-Fusion platform, where dye-labeled oligo reporters (complementary to the barcodes) are hybridized to the barcode to visualize the antibody. Antibody concentration, exposure time, and corresponding dye were further optimized, verified, and validated as a whole module using tonsil and cancer tissues based on image analysis and intensity analysis. In this study, we showcased the rigorous process used for development and validation of PhenoCode™ Discovery panels and demonstrated how they can work together to collect comprehensive data from one tissue sample. The introduction of these pre-optimized antibody panels marks a pivotal point in the evolution of ultrahigh-plex spatial phenotyping.

Abstract 4649: Design and validation of ultrahigh-plex discovery panels for immuno-oncology and oncology

Abstract Introduction Ultrahigh-plex single-cell spatial phenotyping is revolutionizing cancer research through deeper interrogation of cellular and protein-level co-expression, localization, and arrangements within cancer tissues. To enable in-depth characterization of the tumor microenvironment (TME), we have developed and validated pre-optimized antibody panels on cancer tissues to reveal key cell types and cellular architecture using the PhenoCycler™-Fusion system. Here, we present our methodology for the development and validation of multiplex spatial phenotyping panels for oncology and immuno-oncology. Methods We designed panel modules such that each module answers specific biological questions about the TME and can be multiplexed together for a more comprehensive view of the structure and function of the tumor and TME. The resulting PhenoCode™ Discovery panel modules include markers that identify key immune cell types, immune activations and checkpoints, mediators of proliferation, and other hallmarks of cancer. Each antibody in the panel module was conjugated to an oligo barcode and stained on human formalin-fixed paraffin-embedded (FFPE) tissue. The slide was imaged on a PhenoCycler™-Fusion platform, where dye-labeled oligo reporters (complementary to the barcodes) are hybridized to the barcode to visualize the antibody. The results were compared serial sections run with a 3,3’-Diaminobenzidine (DAB) chromogenic immunohistochemistry assay to ensure specificity of the antibody was retained. Antibody concentration, exposure time, and corresponding dye were further optimized, verified, and validated as a whole module using tonsil and cancer tissues based on image analysis and intensity analysis, to achieve an ultrahigh-plex detection. Results and Conclusions In this study, we showcased the rigorous process used for development and validation of PhenoCode™ Discovery panel modules and demonstrated how they can seamlessly work together to collect more data out of one tissue sample. The introduction of these pre-optimized antibody panel modules mark a pivotal point in the evolution of ultrahigh-plex spatial phenotyping. They not only simplify experimental design and assay set up, with panels that make it easier for researchers to answer important questions with this technology, but also save precious time and resources, while reducing overall costs. Citation Format: Yan He, Shannon Berry, Olive Shang, Michael McLane, Nicholas Ihley, Yi Zheng. Design and validation of ultrahigh-plex discovery panels for immuno-oncology and oncology. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4649.

Phenotypic Characterization and Heterogeneity among Modern Clinical Isolates of Acinetobacter baumannii.

Acinetobacter baumannii is an opportunistic pathogenic bacterium prioritized by WHO and CDC because of its increasing antibiotic resistance. Heterogeneity among strains represents the hallmark of A. baumannii bacteria. We wondered to what extent extensively used strains, so-called reference strains, reflect the dynamic nature and intrinsic heterogeneity of these bacteria. We analyzed multiple phenotypic traits of 43 nonredundant, modern, and multidrug-resistant, extensively drug-resistant, and pandrug-resistant clinical isolates and broadly used strains of A. baumannii. Comparison of these isolates at the genetic and phenotypic levels confirmed a high degree of heterogeneity. Importantly, we observed that a significant portion of modern clinical isolates strongly differs from several historically established strains in the light of colony morphology, cellular density, capsule production, natural transformability, and in vivo virulence. The significant differences between modern clinical isolates of A. baumannii and established strains could hamper the study of A. baumannii, especially concerning its virulence and resistance mechanisms. Hence, we propose a variable collection of modern clinical isolates that are characterized at the genetic and phenotypic levels, covering a wide range of the phenotypic spectrum, with six different macrocolony type groups, from avirulent to hypervirulent phenotypes, and with naturally noncapsulated to hypermucoid strains, with intermediate phenotypes as well. Strain-specific mechanistic observations remain interesting per se, and established "reference" strains have undoubtedly been shown to be very useful to study basic mechanisms of A. baumannii biology. However, any study based on a specific strain of A. baumannii should be compared to modern and clinically relevant isolates. IMPORTANCE Acinetobacter baumannii is a bacterium prioritized by the CDC and WHO because of its increasing antibiotic resistance, leading to treatment failures. The hallmark of this pathogen is the high heterogeneity observed among isolates, due to a very dynamic genome. In this context, we tested if a subset of broadly used isolates, considered "reference" strains, was reflecting the genetic and phenotypic diversity found among currently circulating clinical isolates. We observed that the so-called reference strains do not cover the whole diversity of the modern clinical isolates. While formerly established strains successfully generated a strong base of knowledge in the A. baumannii field and beyond, our study shows that a rational choice of strain, related to a specific biological question, should be taken into consideration. Any data obtained with historically established strains should also be compared to modern and clinically relevant isolates, especially concerning drug screening, resistance, and virulence contexts.

MEPSi: A tool for simulating tomograms of membrane-embedded proteins

The throughput and fidelity of cryogenic cellular electron tomography (cryo-ET) is constantly increasing through advances in cryogenic electron microscope hardware, direct electron detection devices, and powerful image processing algorithms. However, the need for careful optimization of sample preparations and for access to expensive, high-end equipment, make cryo-ET a costly and time-consuming technique. Generally, only after the last step of the cryo-ET workflow, when reconstructed tomograms are available, it becomes clear whether the chosen imaging parameters were suitable for a specific type of sample in order to answer a specific biological question. Tools for a-priory assessment of the feasibility of samples to answer biological questions and how to optimize imaging parameters to do so would be a major advantage. Here we describe MEPSi (Membrane Embedded Protein Simulator), a simulation tool aimed at rapid and convenient evaluation and optimization of cryo-ET data acquisition parameters for studies of transmembrane proteins in their native environment. We demonstrate the utility of MEPSi by showing how to detangle the influence of different data collection parameters and different orientations in respect to tilt axis and electron beam for two examples: (1) simulated plasma membranes with embedded single-pass transmembrane αIIbβ3 integrin receptors and (2) simulated virus membranes with embedded SARS-CoV-2 spike proteins.

Meeting in the Middle: Towards Successful Multidisciplinary Bioimage Analysis Collaboration.

With an increase in subject knowledge expertise required to solve specific biological questions, experts from different fields need to collaborate to address increasingly complex issues. To successfully collaborate, everyone involved in the collaboration must take steps to “meet in the middle.” We thus present a guide on truly cross-disciplinary work using bioimage analysis as a showcase, where it is required that the expertise of biologists, microscopists, data analysts, clinicians, engineers, and physicists meet. We discuss considerations and best practices from the perspective of both users and technology developers, while offering suggestions for working together productively and how this can be supported by institutes and funders. Although this guide uses bioimage analysis as an example, the guiding principles of these perspectives are widely applicable to other cross-disciplinary work.

Recent advances in spatially resolved transcriptomics: challenges and opportunities

Single-cell RNA sequencing (scRNA-seq) has greatly advanced our understanding of cellular heterogeneity by profiling individual cell transcriptomes. However, cell dissociation from the tissue structure causes a loss of spatial information, which hinders the identification of intercellular communication networks and global transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms that preserve spatial information have been actively developed. Significant achievements in imaging technologies have enabled in situ targeted transcriptomic profiling in single cells at single-molecule resolution. In addition, technologies based on mRNA capture followed by sequencing have made possible profiling of the genome-wide transcriptome at the 55-100 μm resolution. Unfortunately, neither imaging-based technology nor capture-based method elucidates a complete picture of the spatial transcriptome in a tissue. Therefore, addressing specific biological questions requires balancing experimental throughput and spatial resolution, mandating the efforts to develop computational algorithms that are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-art spatially resolved transcriptomic technologies, describe their applications in a variety of biological domains, and explore recent discoveries demonstrating their enormous potential in biomedical research. We further highlight novel integrative computational methodologies with other data modalities that provide a framework to derive biological insight into heterogeneous and complex tissue organization.

Quantifying and Cataloguing Unknown Sequences within Human Microbiomes.

ABSTRACTAdvances in genome sequencing technologies and lower costs have enabled the exploration of a multitude of known and novel environments and microbiomes. This has led to an exponential growth in the raw sequence data that are deposited in online repositories. Metagenomic and metatranscriptomic data sets are typically analysed with regard to a specific biological question. However, it is widely acknowledged that these data sets are comprised of a proportion of sequences that bear no similarity to any currently known biological sequence, and this so-called “dark matter” is often excluded from downstream analyses. In this study, a systematic framework was developed to assemble, identify, and measure the proportion of unknown sequences present in distinct human microbiomes. This framework was applied to 40 distinct studies, comprising 963 samples, and covering 10 different human microbiomes including fecal, oral, lung, skin, and circulatory system microbiomes. We found that while the human microbiome is one of the most extensively studied, on average 2% of assembled sequences have not yet been taxonomically defined. However, this proportion varied extensively among different microbiomes and was as high as 25% for skin and oral microbiomes that have more interactions with the environment. A rate of taxonomic characterization of 1.64% of unknown sequences being characterized per month was calculated from these taxonomically unknown sequences discovered in this study. A cross-study comparison led to the identification of similar unknown sequences in different samples and/or microbiomes. Both our computational framework and the novel unknown sequences produced are publicly available for future cross-referencing. Our approach led to the discovery of several novel viral genomes that bear no similarity to sequences in the public databases. Some of these are widespread as they have been found in different microbiomes and studies. Hence, our study illustrates how the systematic characterization of unknown sequences can help the discovery of novel microbes, and we call on the research community to systematically collate and share the unknown sequences from metagenomic studies to increase the rate at which the unknown sequence space can be classified.

Analytical comparison of absolute quantification strategies to investigate the insulin signaling pathway in fat cells.

So far, mass spectrometry-based targeted proteomics is the most sensitive approach to answer and address specific biological questions in an accurate and quantitative fashion. However, the data analysis design used for such quantification varies in the field leading to discrepancies in the reported values. In this study, different quantification strategies based on calibration curves were evaluated and compared. The best accuracy and coefficient of variation was achieved by ratio to ratio calibration curves. We applied the ratio to ratio quantification approach to analyze very low abundant insulin signaling proteins such as PIK3RA (0.10-0.93 fmol/μg), AKT1 (0.1-0.39 fmol/μg), and the insulin receptor (0.22-2.62 fmol/μg) in a fat cell model and demonstrated the adaptation of this pathway at different states of insulin sensitivity.

Emerging models: The "development" of the ctenophore Mnemiopsis leidyi and the cnidarian Nematostella vectensis as useful experimental models.

The goal of this chapter is to explain the reasoning for developing two understudied invertebrate animal species for asking specific biological questions. The first is the ctenophore (comb jelly) Mnemiopsis leidyi and the second is the anthozoan cnidarian (starlet sea anemone) Nematostella vectensis. Although these two taxa belong to some of the earliest branching extant metazoan clades, their developmental features could hardly be more different from one another. This should serve as a general warning to be careful when extrapolating comparisons of one species to another. Two-taxon comparisons are especially flawed; and to interpret features in a phylogenetic context one must sample carefully within a given taxon to determine how representative certain features are before comparing with other clades. The other benefit of this comparison is to identify key practical factors when attempting to develop new species for experimental investigation.

Nextcast: A software suite to analyse and model toxicogenomics data

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

Assessing the Response of Human NK Cell Subsets to Infection by Clinically Isolated Virus Strains.

Natural killer (NK) cells play a critical role in defending against virus infections.Investigating human NK cell antiviral functions is of prime importance; however, there are challenges such as the human-specific nature of many viruses and differences in NK cell surface markers between humans and rodents. Research on the antivirus response of human NK cells must therefore be carefully planned around species tropism of the viruses of interest and the specific biological questions to be answered. The initial site of many virus infections is a mucosal/epithelial surface. In this context, a clinical virus infection at the ocular surface enables direct analyses on the mechanisms and consequences of infection and immune reactions in situ over the course of disease. For example, the site of infection of a clinical infection in the conjunctiva and cornea can be directly observed in real-time, utilizing split-lamp microscopy, and specimens are readily accessed with minimally invasive techniques.In this chapter, we describe protocols for investigating NK cell responses using clinically isolated viruses in co-culture assays. We also describe procedures for ex vivo analysis of conjunctiva-derived NK cells in adenovirus infection.