The integration of single-cell technologies with computational techniques marks the dawn of a new era in therapeutic discovery, enabling unprecedented precision in understanding immune dynamics and disease mechanisms. Perturbation-based studies are pivotal in this context, offering systematic frameworks to explore immune system responses to therapeutic interventions. The accurate prediction of perturbation outcomes is crucial for uncovering key regulatory mechanisms and designing specific, targeted therapeutic strategies.

By integrating cutting-edge experimental methodologies with advanced computational innovations this collection aims to advance the frontier of cell-targeted precision medicine through perturbation-guided target identification, laying the foundation for transformative therapeutic strategies.

This Research Topic emphasizes a dual focus: experimental approaches to generate and analyze perturbation-based data, and computational methodologies to interpret and predict the outcomes. The single-cell-oriented modalities include, but are not limited to, transcriptomics, epigenomics, spatial, and proteomics:

Experimental: This sub-theme highlights innovative experimental approaches that provide mechanistic insights through perturbation-based studies:

• Application of single-cell CRISPR screens to dissect immune regulatory pathways and identify potential therapeutic targets.
• Multiplexed perturbation studies (e.g., Perturb-seq, CITE-seq, pooled genetic screens) to characterize immune cell functions and dynamics
• Experimental strategies for reprogramming immune cells to modulate their behavior (e.g., reversing T-cell exhaustion, polarizing macrophages).
• Perturbation experiments to predict and optimize therapeutic responses, including CAR-T therapies, immune checkpoint inhibitors, and vaccines.
• Development of in vitro and in vivo models to detect cell type-specific biomarkers associated with prognosis in immune-related therapeutic interventions.
• Capturing the temporal dynamics of immune responses across cell types to investigate perturbed cell behaviors and states in diseased conditions.

Computational: This sub-theme focuses on the application of machine learning (ML) and artificial intelligence (AI) approaches to analyze and predict the outcomes of perturbation-based studies:

• Models for predicting transcriptional and phenotypic outcomes of immune perturbations.
• Generative frameworks to simulate transcriptional changes or phenotypic adaptations in response to perturbations.
• Algorithms for perturbation-guided identification of druggable targets and therapeutic opportunities.
• Network-based approaches to predict immune system rewiring following therapeutic interventions.
• Computational simulations of immune regulatory networks to assess perturbation effects at a systems level.
• Synthetic perturbation design tools to optimize immune reprogramming strategies.
• Integration of multi-omics data to refine predictive models and enhance biomarker strategies for improved patient care.

Topic Editor Dr Nima Nouri is employed by AstraZeneca and Topic Editor Dr Hamid Mattoo is employed by Sanofi U.S. All other Topic Editors declare no competing interests with regards to the Research Topic subject.

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Experimental and Computational Advances for Perturbation-Guided Target Identification at Single-Cell Resolution