Friday, May 16 2014 |
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session 3 |
Session 3b: Microgenomics and microRNA and NGS |
Chairman: Michael W. Pfaffl Chariman: Michael W. Pfaffl |
9:00-9:45 | Keynote lecturer | Exosome isolation and holistic expression profiling using RNA-Seq and RT-qPCR | Michael W. Pfaffl, Technical University of Münich, Germany | Abstract Pfaffl |
9:45-10:15 | Invited lecturer | Measuring microRNA expression in size-limited samples | Kai Peter Höfig, Munich University, Germany | |
10:15-10:25 | | Towards a comprehensive single cell expression profiling | Herbert Auer, Spain | |
10:25-10:35 | | A combination of microgenomics approaches to understand the ipmpact of gut microbiota on the regulation of the hypothalamo-pituitary-adrenal axis in rats | Bénédicte Langellier, France | |
10:35-11:15 | | Coffee break - posters | | |
11:15-11:45 | Invited lecturer | Comparative analysis of RNA sequencing methods for degraded or low-input samples | Rahul Satija, Broad Institute of MIT, Massachusetts, USA | |
11:45-11:55 | | Low RNAseq microdissected plant tissue | Bernard Dubreucq, France | |
11:55-12:10 | | GOLD SPONSOR presentation: "Genomic Characterization of Challenging or Limited Research Samples using the Ion AmpliSeq™ Targeted Re-Sequencing Technology" | Dr. Stephen Jackson, Life Technologies/ Life Technologies/Thermo Fischer Scientific, USA | |
12:10-14:00 | | Lunch | | |
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session 4 |
Session 4: Microgrenomics and Proteins |
Chairman:Lance Liotta |
14:00-14:45 | Keynote lecturer | Advantages or disadvantages of different methods for proteomics analysis | Lance Liotta, George Mason University, USA | |
14:45-14:55 | | Oocytomics: combining transcriptomics and proteomics to understand post-transcriptional regulation in bovine oocytes | Rozenn Dalbies-Tran, France | |
14:55-15:05 | | Combining surface plasmon resonance and mass spectrometry to identify Bone Morphogenetic Protein (BMP) interactants | Catherine Taragnat, France | |
15:05-15:35 | | Coffee break - Posters | | |
15:35-16:05 | Invited lecturer | Mass Spectrometry Imaging coupled to Microproteomics: From Imaging to identification of proteins on tissue section | Maxence Wisztorski, University of Lille I, France | |
16:05-16:35 | Invited lecturer | Unlocking the prognostic significance of microdissected proximal lesions from human colon | Daniel W. Rosenberg, University of Connecticut Health Center USA | |
16:35-16:45 | | Closing remarks | | |
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Abstracts |
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| Characterization of rare immune cell types through gene expression profilingDALOD M1The mammalian immune system encompasses a variety of cell types endowed with specific, complementary, functions. The definition of what are distinct cell types as opposed to what are different developmental or activation states of a given cell type is not always easy to make. Only very few cell surface molecules are each expressed in a specific manner on a given immune cell type under steady state conditions. This is even worse under activation conditions. Therefore, the use of a small number of cell surface markers as previously done by “oligoparameter” flow cytometry approaches can sometimes be deceiving and lead to erroneous interpretations regarding cell type identity. Moreover, the markers used to define cell types can considerably vary depending on the mammalian species studied, including between mice and humans. Studying ontogeny to define cell type identity is considered as the gold standard approach in mice but is extremely difficult to do in animal species not amenable to genetic manipulation for in vivo inactivation of transcription factors or for cell-fate mapping experiments. Functional studies are the most informative approaches to understand cell type identity and the most relevant for translational purposes, however such studies require prior knowledge on the potential functional specialization of the cells to select the adequate functional tests to perform, are often costly, time-consuming and the less well standardized. With the development of high throughput approaches for characterizing the gene expression programs of cells even when starting with very low amounts of input material, it has become possible to seek for a less subjective, more rigorous, potentially unbiased definition of cell types based on expression of complex transcriptomic signatures of tens or hundreds of genes. The analysis of these transcriptomic signatures will then help generating new hypotheses on the ontogeny and functions of cell types that can be tested experimentally. We and others demonstrated the power of transcriptomics for solving the identity of cell types harboring an ambiguous cell surface phenotype (1-5) and for translating knowledge on immune cell subset ontogeny and functions from mice to humans or other species (1, 6-9). However, performing gene expression profiling of a given immune cell type is generally achieved after purifying this population using the conventional, biased approaches relying on its phenotypic definition by the combination of a small number of cell surface markers. In other words, in the initial phases of gene expression profiling studies of immune cell types, the approach is still limited by the biases linked to the classical phenotypic definition of immune cells. Hence, during the corresponding sampling procedures, critical steps must be followed to decrease a priori the risk of contamination by another cell type or even of erroneous definition of the cell identity. Additional control steps are required at the time of analysis of the transcriptomics data to re-evaluate these risks a posteriori. These steps will be described through concrete illustrations and discussed with regards to different sampling methods available to purify immune cell types, discussing their respective advantages and drawbacks as well as their complementarity. Once first series of gene expression profiling have been obtained, this helps refining the phenotypic definitions of cell types by identifying combinations of cell surface markers more specific for each cell type and eventually conserved across tissues, activation conditions and species (1, 6-14). If required, this can then allow generating even more robust transcriptomics data for cell types that had not yet been defined with sufficient rigor. Finally, we will discuss the advances that are being brought in this research field through single cell transcriptomic analyses as recently illustrated by other research teams (15). References1) Robbins et al. Genome Biology. 2008; 2) Reynders et al. EMBO J. 2011; 3) Segura et al. Immunity. 2013; 4) Tamoutounour et al. Immunity. 2013; 5) Bar-On et al. PNAS. 2010; 6) Crozat et al. J. Exp. Med. 2010; 7) Contreras et al. J. Immunol. 2010; 8) Guilliams et al. Eur. J. Immunol. 2010; 9) Vu Manh et al. J. Immunol. 2014; 10) Crozat et al. J. Immunol. 2011; 11) Vu Manh et al. Eur J Immunol. 2013; 12) Cros et al. Immunity. 2010; 13) Miller et al. Nature Immunol. 2012; 14) Gautier et al. Nature Immunol. 2012; 15) Jaitin et al. Science. 2014. 1Centre d’Immunologie de Marseille-Luminy (CIML), Aix-Marseille Université UM2, Inserm UMR1102, CNRS UMR7280, 13288 Marseille, France. Email: dalod@ciml.univ-mrs.frBack to Session 1 |
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DeCIPHEr critical steps in a flow cytometry experiment to ensure reproducible and high quality microgenomic datasetLUCHE H1, HADJEM L1, MELLO M1, GRENOT P1, MALISSEN B1,2,3,4, MALISSEN M1,2,3,4Recent years have witnessed the growth of functional genomics projects focusing on the use of the laboratory mouse as a model of human disease. The single gene approach has lasted, and we are now entering a new phase where scientists will think in terms of networks and pathways. The Centre for ImmunoPHEnomics - CIPHE (INSERM/US012, AMU, CNRS/UMS3367) is a new institute dedicated to phenogenomics studies. With its cutting-edge expertise in mouse genetics and immunology, the CIPHE aims to develop and analyze in a massively parallel and standardized mode mouse KO/KI models allowing understanding of the function of the mouse immune system under normal and infectious conditions.
The main investigation technique of the CIPHE immune-phenotyping module is cytometry. By combining our expertise in flow cytometry and knowledge in immunology, we establish high content immunophenotyping panels (>14C) to investigate all cell subsets of the hematopoietic lineage in the mouse. Thanks to the release of a broad range of bright new reagents and the availability of high-end instruments able to monitor up to 18 fluorescent parameters at the single cell level, HC-MFC could emerge and allow the entry into a cytomic era. The Immgen experience and studies we have performed in collaboration with CIML have demonstrated that high content multiparameter flow cytometry (HC-MFC) was instrumental in transcriptomic studies on minute populations of highly purified cells. However, setting up HC-MFC experiments is a real technical challenge and each experimental step needs to be carefully examined and controlled to ensure meaningful analysis. In light of our contributive work to the Immgen2 consortium, we will go through all the critical steps in a flow cytometry and cell sorting experiment that need to be considered to ensure reproducible and high quality microgenomic datasets.1. The Centre for ImmunoPHEnomics – CIPHE, INSERM/US012, AMU, CNRS/UMS33672. Centre d'Immunologie de Marseille-Luminy (CIML), Aix-Marseille University, UM2, Marseille, France,3. Institut National de la Santé et de la Recherche Médicale (INSERM), U1104, Marseille, France,4. Centre National de la Recherche Scientifique (CNRS), UMR7280, Marseille, FranceEmail: herve.luche@inserm.frBack to Session 1 |
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| Circulating Tumor Cells: from cell enrichment to single-cell sequencingPinzani P1IntroductionThe investigation of circulating tumor cells (CTC) would be of great importance for the understanding of the metastatic process. The detection and characterization of CTC is technically demanding, but it would be of great help to achieve early diagnosis, as well as to evaluate the risk of development of distant metastases and to predict relapse. We evaluated different approaches for the study of CTC, looking for a method that is highly sensitive, reproducible and easy to implement in a clinical setting.Materials and MethodsqPCR was used to quantify tumor-related transcripts; we adopted filtration based methods to detect and count CTC and subsequent CTC characterization by immunocytochemical methods. Molecular characterization of CTC from filters imposes the use of sophisticated methods for CTC recovery (i.e. laser microdissection), sensitive and specific downstream molecular analysis. CellSearch® immunomagnetic capture combined with DEPArray™ technology was used to assess the feasibility of single CTC detection and recovery followed by whole genome amplification and sequencing analysis.Results and DiscussionCTC could be successfully detected by all the methods in different cohorts of patients (breast cancer, cutaneous and uveal melanoma, adrenocortical carcinoma). Immunohistochemistry was suitable to characterize CTC on filters, confirming their origin from the respective tumors, and laser assisted microdissection allowed CTC recovery and molecular analysis of genetic alterations and mRNA expression. CTC heterogeneity was detected when mutational analysis of PIK3CA (exons 9 and 20) was performed on 115 single CTC from 18 metastatic breast cancer patients by CellSearch and Deaparray Technology. Six patients (33%) had a PIK3CA mutation identified, including one patient with loss of heterozygosity and one patient with three different PIK3CA variants on single CTC.ConclusionsThis study demonstrates that the molecular characterization of pure CTC samples represents a non-invasive approach to study cancer progression and metastasis. The analysis of single cancer cells is likely to improve three major themes of oncology: detection, progression, and prediction of therapeutic efficacy. Technical advances have enabled genomic analyses at the single-cell level allowing the profiling of rare cancer cells in clinical samples.1 Dip. Scienze Biomediche, Sperimentali e Cliniche., Università degli Studi di Firenze, viale Pieraccini 6, 50139,Frienze, Italy. Email: p.pinzani@dfc.unfi.it.Back to Session 2 |
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High throughput single cell expression profiling KUBISTA M.,1,2 RUSNAKOVA V.,2 SIDOVA M.,2 SINDELKA R.,2 FOROOTAN A.,3 SJÖGREEN B.,3 STÅHLBERG A.1,4 IntroductionBiological samples are complex, being composed of large numbers of cells of different types. When studying traditional samples containing many cells only the collective response of all the cells present is measured. However, the cells may respond differently and a small subpopulation may be critical. Today, these systems can be studied using single cell expression profiling. Here we apply single cell profiling to study the response of astrocytes to brain trauma using a mouse model. We also study asymmetric cell division during early development of Xenopus laevis by single cell and intracellular profiling using qPCR tomography.Materials and MethodsSingle astrocytes were collected by FACS from mice expression GFP under GFAP promoter, while blastomeres were collected by aspiration. Cells were lysed (Cellulyser, TATAA Biocenter), reverse transcribed (GrandScript, TATAA Biocenter), pre-amplified (GrandMaster PreAmp, TATAA Biocenter), and profiled using high throughput microfluidic qPCR (BioMark, Fluidigm). Data were pre-processed and cells were classified using multivariate methods (PCA, SOM, clustering) and correlation analysis with the GenEx software (ver. 6, MultiD Analysis).Results and DiscussionAstrocytes were collected from mouse brains at different time points after the induction of focal ischemia. Each cell was profiled for the expression of 47 genes. Classification revealed astrocyte reactivation with the formation of distinct subtypes (Figure). Single cell and subcellular blastomere profiling revealed asymmetric cell division is induced by asymmetric distribution of key cell fate determinants already in the fertilized cell.ConclusionsSingle cell profiling is most powerful to study complex biological samples, revealing heterogeneity and to identify key expression pathways active in critical cell types. Power and robust flows for experimental and analytical analysis are available, as well as highly optimized reagents.ReferencesM. Bengtsson, A. Ståhlberg, P. Rorsman, and M. Kubista. Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Research 15, 1388 (2005).Anders Ståhlberg, Mikael Kubista. The workflow of single cell profiling using qPCR. Expert Rev. Mol. Diagn. 14(3).
1 TATAA Biocenter (www.tataa.com), Gothenburg, Sweden.
2 Institute of Biotechnology, Czech Academy of Sciences, Czech Republic
3 MultiD Analyses (www.multid.se), Gothenburg, Sweden
4 The Cancer Center, Gothenburg University, Gothenburg, Sweden Email: Mikael.kubista@tataa.comBack to Session 3a |
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| Exosome isolation and holistic expression profiling using RNA-Seq and RT-qPCRPfaffl M W1, Kirchner B IntroductionSmall RNAs, in particular microRNAs, regulate gene expression by post transcriptional binding and thereby suppressing protein translation. They are present in most eukaryotic cells and play an important role in almost all physiological or regulative processes. Small RNAs were detected in various matrices, such as blood, plasma, saliva and urine. However, very less information is available about the small RNA composition in biofluids such as milk and whether milk possesses its own defined small RNA profile differing from blood. Further the small RNA transcriptome differences between whole milk versus milk exosomal isolates were investigated.Materials and MethodsTo generate a holistic overview of all present small RNAs Next Generation Sequencing (small RNA-Seq) was performed on whole blood, whole milk and exosomes during the first lactation days. Exosomes were purified via ultracentrifugation, due to the higher exosomal and RNA quality. Small RNA-Seq was performed using an Illumina HiSeq 2500 and subsequent data analysis was done independently, using either the GGS and GGA stations from Genomatix Software GmbH (Munich, Germany) or using freely available python scripts and R-packages (Bioconductor). First focus was on the dynamic regulation of microRNAs in milk and/or exosomes in comparison to blood. Significantly regulation of microRNAs between different tissues and time points was defined by a fold change of expression of at least 2-fold and a Benjamini-Hochberg adjusted p-value of less than 0.05. To validate these findings key microRNAs were quantified via RT-qPCR for fold change comparisons. Experimentally validated mRNA targets for regulated microRNAs were taken from the Tarbase 6.0 database from Diana Lab (Athens, Greece) and pathway analyses were generated using GePS (Genomatix Pathway System).Results and DiscussionRNA Seq clearly showed that milk and exosomes possesses its own unique small RNA profile compared to blood. It highlights its dynamic changes during the first lactation days. Pathway analysis for affected targets revealed significant impact on cell cycle progression, cell adhesion, DNA repair, apoptosis, and oncogenic defense. This study underlines the potential role of microRNAs (and small RNAs in general) in mammary gland physiology. Milk microRNAs and exosomes seem essential for the mammary gland immune system, but also as an active modulator in the newborn calf.1Physiology Weihenstephan, ZIEL Research Center for Nutrition and Food Sciences,Technische Universität München, Freising, GermanyEmail: michael.pfaffl@wzw.tum.deBack to Session 3b |
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