Preprint Club
A cross-institutional Journal Club Initiative

Keywords

• Inflammation

• Single Cell Seq

• Circulating Immune Cell Atlas

• Patient Classifier

• Precision Medicine

Main Findings

Inflammation is an intricate physiological response, regulating both homeostatic processes and responses to pathological conditions. Despite considerable advancements in deciphering acute and chronic disease-specific inflammatory mechanisms, achieving a comprehensive understanding of inflammation remains challenging. This preprint by Jiménez-Gracia et al. leverages advancements in single-cell genomics to unravel the spectrum of immune cell activation during inflammatory processes associated with infections, immune-mediated inflammatory diseases, and cancer. The authors conducted an analysis of 2 million peripheral blood cells obtained from a cohort of 356 patients spanning 18 diseases to identify inflammation-related patterns. This aimed to characterize disease-specific signature gene sets. Based on these signature gene sets the authors developed a single cell atlas for circulating immune cells in immune-mediated inflammatory diseases, acute inflammation conditions, chronic inflammatory diseases and in solid tumours.To construct the single cell atlas, the authors initially ranked genes based on their expression patterns relative to cell type and disease. This approach facilitated the identification of inflammation-related patterns and lead to the characterization of signature gene sets across cell types and diseases.

Finally, a systematic pipeline was devised to identify cell-specific expression profiles and differential genes, that distinguish diseases across cell types. This pipeline was validated by splitting the full patient set into training and test data sets without cell annotations, which yielded a high accuracy for disease classification. Utilizing this single-cell atlas, the authors could identify two novel disease-associated genetic biomarkers. Notably, elevated levels of the gene CYBA were observed in intestinal inflammatory diseases among patients with Crohn’s disease and Ulcerative colitis, while reduced CYBA expression was evident in skin-related inflammations such as Psoriasis and Psoriatic Arthritis. Additionally, an increased expression of IFITM1 was detected in lymphoid cells of individuals with Chronic Obstructive Pulmonary Disease (COPD), suggesting its potential as a marker gene. 

These findings illustrate the practical utility of the atlas. While current medical practices enable effective differentiation among disease groups without relying on these biomarkers, future applications of this pipeline hold promise in refining disease categorization within diagnosed conditions. This, in turn, may facilitate the delivery of precise therapies to patients based on their position along the disease spectrum.

Limitations

• It is uncertain if the batch correction and data integration for the pool of samples sourced from various locations, laboratories and conditions, including datasets from public databases is sufficient.

• The clinical context provided for the patient samples could be more detailed. Defining healthy controls (homeostasis group) and elucidating how they align with datasets concerning disease and age is lacking. Particularly, with increasing age the definitionof healthy controls would be interesting. It is also not evident that the respective treatments for each disease, as well as potential comorbidities and ethnic backgrounds were considered as confounding factors.

• This study lays a foundation that can be further utilized in intra-disease diagnostics. Asmedical practitioners can well distinguish between the disease groups, it would bemore helpful to identify patients within the inflammatory disease subsets. Based onthese more precise diagnostics patient-tailored treatments can be selected.

• For future development of this single cell atlas including and defining the marker expression at different time points throughout disease progression, including diseaseonset, could contribute to early diagnosis.

• For the future it would be interesting to consider age, gender, medical background, ethnic background for the prediction model. In such a follow-up study the identifiednovel biomarkers CYBA and IFITM1 could be further established using more elaborate data sets.

Significance/Novelty

• Classification of patients into disease groups based on gene signatures from blood samples.

• Identification of novel biomarkers across inflammation conditions can potentially be apath to improved diagnostics and patient-tailored treatments by subclassfication within the disease group.

Credit

Reviewed by Carolin Knappe and Yaakov Gershtein, as part of a cross-institutional journal club between the Vanderbilt University Medical Center (VUMC), the Max-Delbrück Center Berlin, the Medical University of Vienna and other life science institutes in Vienna. 

The author declares no conflict of interests in relation to their involvement in the review.