Gabriella Rustici EMBL-EBI

Transcription

1 Towards a cellular phenotype ontology Gabriella Rustici EMBL-EBI gabry@ebi.ac.uk

2 Systems Microscopy NoE at a glance The Systems Microscopy NoE ( ) is a FP7 funded project, involving 15 research groups across Europe The aim of this consortium is to develop tools and strategies to achieve a systems biology understanding of the living cell Done combining automated fluorescence microscopy, cell microarray, RNAi screening, quantitative image analysis and data mining Capture data and build models in four dimensions, three-dimensional space and time, and measure dynamic events in single living cells Focus on two cellular processes that are highly relevant to human cancer: cell division and cell migration

3 Systems Microscopy NoE at a glance WP7 Translational Applications and Outcome of Systems Microscopy WP8 Development of Standards to Enable Systems Microscopy WP9 Development and Application of a Public Database for Systems Microscopy WP1 Systems Biology Analysis of Cell Division WP2 Systems Biology Analysis of Cell Migration WP3 Development of high throughput imaging and screening platforms to enable production of systems biology data WP4 Development of software for automated multi-dimensional quantitative extraction and analysis of live cell image WP6 Development and Application of Modeling Methods for Systems Microscopy WP5 Development of Statistics and Bioinformatics Tools for Multidimensional Image-based Data

4 What is the current state of this field? Systems microscopy has not achieved the degree of standardization of other omics approaches We need reporting standards, to adequately capture experimental information. We need a repository for quantitative data derived from images (and not a collection of raw image datasets!) that will: 1. provide access to data for the broader research community 2. accelerate the development of analytical methods for this field and 3. promote the integration of independent systems microscopy studies

5 What metadata/data do we need to capture? study description including study general information and specific screen information, including protocols sirna library information study results as a list of sirnas, associated phenotypes and scores used to assign phenotypes to each sirna

6 Example of study results GENE SYMBOL ENSEMBL ID sirna ID DEVIATION CELL NUMBER VALID PLATES % INHIBITOR TRANSCRIPTS HIT TRANSCRIPT ID(S) Phenotype ALAS1 ENSG /8 ENST /ENSmild inhibition of secretion TOR1AIP1 ENSG /8 ENST /ENSmild inhibition of secretion TRAPPC5 ENSG /3 ENST /ENSmild inhibition of secretion KCTD10 ENSG /3 ENST /ENSmild inhibition of secretion ZNF252 ENSG /4 ENST /ENSmild inhibition of secretion ANXA5 ENSG /4 ENST /ENSmild inhibition of secretion TRAM1L1 ENSG /1 ENST mild inhibition of secretion RSAD2 ENSG /2 ENST mild inhibition of secretion SMC2 ENSG /5 ENST /ENSmild inhibition of secretion C16orf79 ENSG /1 ENST mild inhibition of secretion OR2T1 ENSG /1 ENST mild inhibition of secretion PCDHB8 ENSG /1 ENST mild inhibition of secretion MYL6B ENSG /2 ENST mild inhibition of secretion POLG2 ENSG /1 ENST mild inhibition of secretion ING1 ENSG /5 ENST /ENSmild inhibition of secretion EMILIN3 ENSG /1 ENST mild inhibition of secretion KCTD12 ENSG /2 ENST /ENSmild inhibition of secretion GABARAPL1 ENSG /2 ENST mild inhibition of secretion SQLE ENSG /4 ENST /ENSmild inhibition of secretion PITRM1 ENSG /11 ENST /ENSmild inhibition of secretion LMF2 ENSG /4 ENST /ENSmild inhibition of secretion CHD3 ENSG /8 ENST /ENSmild inhibition of secretion BAT2L1 ENSG /9 ENST /ENSmild inhibition of secretion SLFN13 ENSG /6 ENST /ENSmild inhibition of secretion DLX6 ENSG /3 ENST /ENSmild inhibition of secretion SHE ENSG /1 ENST mild inhibition of secretion PVALB ENSG /6 ENST /ENSmild inhibition of secretion

7 Search types currently supported The current interface prototype supports 5 basic type of searches: 1. for a gene, by gene symbol or Ensembl IDs; 2. for a reagent or sirna, by manufacturer or internal screen ID; 3. for a gene attribute, using Gene Ontology terms; 4. for a phenotype, or multiple phenotypes, within an individual screen and across screens; and 5. for a study, using keywords.

8 Gene summary view Provides information on a gene and the phenotypes associated with the silencing of the selected gene, across independent screens

9 Reagent summary view Provides information on a sirna reagent and the phenotypes associated with it, across independent studies

10 Phenotype summary view across screens Provides a list of genes, whose silencing with a specific reagents, has given rise to a particular set of phenotypes, across screens

11 Challenges Integrate data derived from independent studies and provide a meaningful representation of the experimental results Two levels of integration: 1. At the quantitative level, through the development of pipelines for data analysis, and 2. At the level of phenotypic descriptions, through the development of an ontology for cellular phenotypes An ontology would help to resolve naming ambiguities (i.e. large nucleus vs large nuclei) as well as group related phenotypes together (i.e. mitotic phenotypes), facilitating the integration of independent datasets at the level of phenotypic description

12 Can we integrate phenotypic descriptions? Fuchs et al, 2010, Molecular Systems Biology 6: 370 Neumann, Walter et al, 2010, Nature 464:721

13 Cellular Phenotype Ontology (CPO) Pre-composed ontology based on terms from GO BP, GO CC, GO extensions and PATO Split into structural (morphological) and physiological (process) abnormalities Mitochondrion (GO: ) Mitochondrion phenotype (CPO:XX ) Mitochondrion normal phenotype Mitochondrion abnormal phenotype Abnormal mitochondrion morphology Abnormal mitochondrion physiology Absence of mitochondrion Hoehndorf R et al. Bioinformatics 2012;28:

14 Cellular Phenotype Ontology (CPO) Physiological abnormalities are split into single and multiple occurrence processes PATO used to refine qualities of each Single occurrence processes Durations (increased or decreased) Participants (increased or decreased) Multiple occurrence processes Abnormal frequency (increased or decreased) Abnormal onset (increased or decreased) Hoehndorf R et al. Bioinformatics 2012;28:

15 Automatic pre-composition Built a beast 140K classes, 220K with imports Good underlying theoretic model Needs extending for Cellular Phenotype Db use case Potential for unrealistic classes EL expressivity Should be scalable with enough computing power Practically a struggle to work with on a modest PC

16 Entities, processes and qualities Cellular component Biological Processes Abnormal Size Cell types Temporal quality Shapes Gene Ontology Biological process Gene Ontology Cellular Component Cell type ontology (CTO) Phenotype and trait ontology (PATO) Absent

17 Composing a phenotype description Entity Quality pattern Entity (a bearer of some quality) Quality (some characteristic of the entity) Phenotype: Large nucleus Entity: nucleus (GO_000xxxx) Quality: large (PATO_000xxxx) Phenotype: Cells stuck in metaphase due to metaphase arrest Entity: mitotic metaphase (GO_ ) Quality: arrested (PATO_ )

18 New strategy for ontology building Annotation tool Ontology Terms Distribute tool to consortium members for phenotype annotation Map the phenotype terms annotated using ontologies to CPO

19 Phenotypes annotation tool Original phenotypic description Ontology based annotations

20 Mapping phenotypes from different biological scales Cellular phenotypes from single cultured mammalian cells Cellular phenotypes from mouse tissues Cellular phenotypes from human tissues Collect terms used to annotate cellular phenotypes in the different domains Map the resulting ontologies onto each other to enable correlative analysis

21 Open questions Sometimes phenotypes are not linked to biological processes because you don t have enough information to make this association We mostly observe cell population phenotypes but the technology is moving towards single cell observations How do we deal with quantitative phenotypes (cell size, nucleus size, actin content, DNA content, )? Existing ontologies might not be granular enough to describe what we want to describe How do we deal with the temporal information?

22 Goal Develop a data driven, generic upper level ontology for cellular phenotypes Open access tool for annotating phenotypes and capture necessary metadata Templates for new terms and ontology extension Pilot study with Systems Microscopy and BioMedBridges scientists to see how they can use a EQ based annotation tool

23 Acknowledgements EMBL-EBI: Catherine Kirsanova, Simon Jupp, James Malone, Alvis Brazma EMBL-Heidelberg: Jean-Karim Heriche, Beate Neumann, Bernd Fisher, Wolfgang Huber, Jan Ellenberg, Christoph Moehl, Mayumi Isokane, Celine Revenu CU: Robert Hoehndorf, George Gkoutos Prototype URL: