HTS-IA: High Throughput Screening Information Architecture for Genomics

HTS-IA: High Throughput Screening Information Architecture for Genomics

Wienand A. Omta (University Medical Center Utrecht, Utrecht, Netherlands), David A. Egan (University Medical Center Utrecht, Utrecht, Netherlands), Judith Klumperman (University Medical Center Utrecht, Utrecht, Netherlands), Marco R. Spruit (Utrecht University, Utrecht, Netherlands) and Sjaak Brinkkemper (Utrecht University, Utrecht, Netherlands)
DOI: 10.4018/ijhisi.2013100102
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Abstract

This paper describes a high throughput screening architecture for functional genomics screens that use high content methods. Case studies were performed using the Yin case study approach. Additionally a detailed process model is provided using a Method Engineering approach. This study shows that current information architecture lacks interchangeability and functionality. Data enrichment is carried out manually, and software is still deficient in term of interoperability so as to be able to successfully gather data from various external sources. This begs for the growing need of a real integrated laboratory information management system both in academia as well as small-to-medium-sized commercial organizations. Current solutions are designed primarily for clinical samples and lack functionality for larger libraries. A solution should give users the ability to create data pipelines that allow processes to be easily reflected in a relational database.
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Introduction

It simply is not practical, and would be error-prone, to investigate a large quantity of reagents manually (Persides, 1998; Allan et al., 2012). High Throughput Screening (HTS) is a process in which large libraries of chemical or biological reagents can be tested for activity in assays using automated methods (Allan et al., 2012). Certainly an intensifying domain for bioinformaticians, HTS may now be conceived as an essential experimental instrument for the analysis of many biological processes. Experiments are conducted using 96, 384 or 1536-well micro plates where cells or proteins and libraries of reagents are added according to a specified protocol. Reagents are stored in micro plates or in individual 2d-barcoded mini-tubes.

High content analysis is a subset of HTS in which images of cells are acquired through the use of automated microscopy. Subsequently, automated image analysis can be used to generate multi-parameter numerical data. Thus, screens generate large amounts of captured data, which require further analysis for the identification of germane outliers, or hits (Pelz, Gilsdorf & Boutros, 2010). HTS is widely used within multiple disciplines including drug discovery, functional genomics and toxicology (Johan et al., 2004; Bajorath, 2002; Maurer, 2000). Functional genomics screens frequently make use of RNA interference technology (RNAi) that allows for the specific reduction or “knock down” of the function of individual genes. Insights can often be gained where domain datasets are innovatively structured and the integration of various datasets is instigated (Johan, 2004). For the current presentation, we will focus on the use of HTS for RNAi screens combined with high content analysis.

Figure 1 depicts a generic model for reagent processing leading to the creation of assay plates that are used for RNAi screens. Oligo stock implies 96 well-plates containing siRNA oligonucleotides bought by the research institute and delivered by a manufacturer such as Thermo Scientific or Ambion. These plates must be processed in order to be useful for screening. In the pooling step, four oligonucleotides that target the same gene are combined in one well. Briefly, one well actually tests four different oligonucleotides. At the same time the single oligonucleotides are also individually stored in oligo pick plates.

Figure 1.

Generic model of reagent processing

A working master plate contains 384 wells and has the same dimensions as a pooled stock or oligo stock. The use of 384-well plates greatly eases the handling of libraries as it reduces the number of plates 16-fold. As well, pooled oligonucleotides are stored in 2D barcoded mini-tubes. These can be cherry picked for individual hit confirmation and the generation of smaller focused screens. siRNA experiments are generally performed at nM concentrations (10−9 mol/dm3). The pooled stock is stored at μM concentrations (10−6 mol/dm3). Thus, an intermediate dilution plate is required to decrease the concentration to 100nM before the creation of assay plates. An assay plate is used for experiments and once the data is derived and analysed, significant outliers or “hits” are selected. As aforementioned, hits are confirmed using the mini-tubes. After the confirmation screen, the individual oligonucleotides are picked from the oligo pick plates to verify the activity associated with each of the four. This is called deconvolution and it provides essential information for the evaluation of hits.

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