HTPathwaySeq is Biogazelle’s proprietary low-cost high-throughput molecular cellular phenotyping service to characterize therapeutic molecules, including small molecules and RNA targeting agents such as siRNAs and antisense oligonucleotides. A typical experiment assesses 96 conditions (in quadruplicate, so 384 samples) based on shallow 3’ end sequencing of crude cell lysates. HTPathwaySeq provides insights in regulated pathways, toxicities, and compound similarities.



Bridging the gap between RNA sequencing and qPCR

There is an unmet need for an expression profiling service that can provide detailed molecular insights in high-throughput and at low cost. Classic RNA sequencing can generate accurate expression data for all genes but is typically applied for few samples at a high cost. At the opposite end of the spectrum, qPCR can quantify few genes but for thousands of samples at a low cost.

Bridging this gap, Biogazelle has developed a new expression profiling workflow that processes 384 cell lysates through shallow 3’ end RNA-sequencing without the need for RNA extraction. The gene expression data is analyzed to generate a comprehensive and unbiased view on differential pathway activity between conditions.

Applications of HTPathwaySeq

Early stages of drug discovery often depend on relatively simple reporter assays or phenotypic readouts, providing little or no information on the drug’s mechanism of action (MOA). Gene expression profiling technologies like RNA-sequencing enable a more comprehensive characterization of compounds by measuring the activity of molecular pathways. This information can complement phenotypic readouts and can be used to prioritize candidate compounds for further drug development. RNA expression profiling also serves as a generic test that can be applied to any drug development pipeline without the need for target-dependent customization.

HTPathwaySeq can provide insights on the mode of action underlying induced cellular phenotypes or reveal potential compound-induced toxicities. It can highlight molecular similarities between compounds and identify those perturbations acting similar to a reference condition or via shared molecular mechanisms.

To assist data interpretation, HTPathwaySeq comes with a suite of data analysis tools that enables easy navigation of results. Users can explore individual compounds and their associated pathways, group pathways based on overlap and significance, or cluster compounds based on similarity of their underlying gene expression profiles.

From the gene expression data, we identify differential pathways using a gene set enrichment algorithm. Over 4000 annotated gene sets are interrogated, representing both canonical pathways and manually curated gene lists from literature involving various chemical and genetic perturbations.

Importantly, our validation of the technology has demonstrated focusing on the most abundant genes does not introduce a bias to differential pathway analysis. Validation of this technology on 3 independent datasets has shown that most of the gene sets identified when using all expressed genes in a more in-depth analysis were also detected when relying only on the 7000 most abundant genes.

HTPathwaySeq can be used to:

  • Identify relevant pathways associated to drug mechanism of action
  • Reveal dose-dependent effects on pathway activity. Differential pathways can be matched with dose-dependent effects on the phenotype to reveal the most relevant pathways regulated by the compound
  • Evaluate compound similarity at the molecular level, leading to better understanding of differences between compounds and their mechanism of action
  • Get insights in potential toxicity of compounds by exploring canonical toxicity pathways like DNA damage or various stress response pathways
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The technology in brief

HTPathwaySeq is performed directly on cell lysates from 96-well culture plates. For robust differential pathway analysis, we typically recommend the inclusion of 4 replicates per condition. As each experiment includes 8 internal controls, up to 94 conditions can be analyzed simultaneously. HTPathwaySeq relies on a 3’ end-sequencing library prep workflow with shallow sequencing (1M reads per sample), resulting in reproducible detection of around 7000 genes per sample. Several data analysis workflows are applied to identify differential pathways in each of the conditions.

Technical performance of HTPathwaySeq
Technical performance of HTPathwaySeq:
A Cumulative distribution of the number of detected genes across 384 samples, with a median of 7000 genes per sample.
B Read coverage is focused at the 3’ end of the genes. 
C Representative reproducibility of gene expression counts between replicate samples.
Download a sample report

Data analysis and visualization using GSEA Explorer

In addition to the standard project report, and in order to facilitate and simplify data analysis, visualization and interpretation, Biogazelle has developed a proprietary app called Savanna. This application consists of 5 user-friendly modules:

  1. Results overview - Get a general overview of the results obtained from gene set enrichment analysis (GSEA) for each of the compounds tested.
  2. Contrast viewer - Explore the enriched gene sets for individual contrasts in addition to informative descriptions and gene details
  3. Toxicity viewer – Evaluate activity of various gene sets associated to canonical toxicity pathways across your siRNAs or ASOs
  4. Similarity viewer - Assess molecular similarity among siRNAs or ASOs based on the enriched gene sets
  5. Leading edge gene overlap explorer - Investigate the leading edge overlap of the top gene sets per contrast

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