Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data

Matthew C Altman ,Darawan Rinchai ,Elizabeth Whalen ,Jacques Banchereau ,Asunción Mejías ,Davide Bedognetti ,Christine M Graham ,Karolina Palucka ,Anne O’garra ,Farrah Kheradmand ,Laurent Chiche ,Virginia Pascual ,Rodolphe Thiébaut ,Octavio Ramilo ,Patricia Thébault ,Mohamed Alfaki ,Aarón Ayllón-Benítez ,Noémie Jourde-Chiche ,Marc Lipman ,Fleur Mougin ,Prasong Khaenam ,Robert J Wilkinson ,Mathieu Garand ,Scott Presnell ,Mohammed Toufiq ,Ganjana Lertmemongkolchai ,J Theodore Phillips ,Chloe Bloom ,Göran B Klintmalm ,Matthew Berry ,Nicole Baldwin ,Basirudeen Syed Ahamed Kabeer ,Damien Chaussabel

doi:10.1038/s41467-021-24584-w

Abstract

As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiological states encompassing 985 blood transcriptome profiles. Interpretation is supported by customized resources, including module-level analysis workflows, fingerprint grid plot visualizations, interactive web applications and an extensive annotation framework comprising functional profiling reports and reference transcriptional profiles. Taken together, this well-characterized and well-supported transcriptional module repertoire can be employed for the interpretation and benchmarking of blood transcriptome profiles within and across patient cohorts. Blood transcriptome fingerprints for the 16 reference cohorts can be accessed interactively via: https://drinchai.shinyapps.io/BloodGen3Module/.

Highlights

As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation
Similar to our first two repertoires (Supplementary Table 1), we included data from patients with: systemic lupus erythematosus (SLE), systemic onset juvenile idiopathic arthritis (SoJIA), liver transplants, and receiving maintenance immunosuppressive therapy, metastatic melanoma, and infectious diseases [with an expanded range that includes infections caused by influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV) infections, Mycobacterium tuberculosis, Staphylococcus aureus, and Burkholderia pseudomallei and sepsis caused by other bacteria (Streptococcus pneumoniae, Salmonella spp., and Pseudomonas aeruginosa)]
Having explained the approach implemented for the construction and characterization of the fixed BloodGen[3] transcriptional module repertoire, we present the analysis and visualization strategies for both group- and individual-level comparisons using an illustrative case focusing on the changes in abundance for module aggregate A28

Summary

Introduction

As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. We describe the development of a new fixed repertoire of transcriptional modules, BloodGen[3], that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. Taken together, this well-characterized and well-supported transcriptional module repertoire can be employed for the interpretation and benchmarking of blood transcriptome profiles within and across patient cohorts. Web applications have been deployed that give users the ability to dynamically generate fingerprint plots for different collections of reference datasets

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Conclusion