In‐silico generation of high‐dimensional immune response data in patients using a deep neural network

• Published in: Cytometry. Part A 2023-05, Vol.103 (5), p.392-404
• Main Authors: Fallahzadeh, Ramin, Bidoki, Neda H., Stelzer, Ina A., Becker, Martin, Marić, Ivana, Chang, Alan L., Culos, Anthony, Phongpreecha, Thanaphong, Xenochristou, Maria, De Francesco, Davide, Espinosa, Camilo, Berson, Eloise, Verdonk, Franck, Angst, Martin S., Gaudilliere, Brice, Aghaeepour, Nima
• Format: Article
• Language: English
• Subjects: Artificial neural networks; autoencoders; Cellular communication; Data collection; Deep learning; generative modeling; Humans; Immune response; Immune system; Machine Learning; mass cytometry; Neural networks; Neural Networks, Computer; Patients; Proteomics; single‐cell Immunome; surgery
• ISSN: 1552-4922; 1552-4930; 1552-4930
• DOI: 10.1002/cyto.a.24709

Technologies for single‐cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever‐increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre‐ and post‐surgical intervention, we demonstrate that the generated patient‐specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness.