CRISPR, as an emerging gene editing technology, has been widely used in multiple fields due to its convenient operation, less cost, high efficiency and precision. This robust and effective device has revolutionized the development of biomedical research at an unexpected speed in recent years. The development of intelligent and precise CRISPR delivery strategies in a controllable and safe manner is the prerequisite for translational clinical medicine in gene therapy field. In this review, the therapeutic application of CRISPR delivery and the translational potential of gene editing was firstly discussed. Critical obstacles for the delivery of CRISPR system in vivo and shortcomings of CRISPR system itself were also analyzed. Given that intelligent nanoparticles have demonstrated great potential on the delivery of CRISPR system, here we mainly focused on stimuli-responsive nanocarriers. We also summarized various strategies for CIRSPR-Cas9 system delivered by intelligent nanocarriers which would respond to different endogenous and exogenous signal stimulus. Moreover, new genome editors mediated by nanotherapeutic vectors for gene therapy were also discussed. Finally, we discussed future prospects of genome editing for existing nanocarriers in clinical settings.

Idiopathic pulmonary fibrosis (IPF) is a devastating lung disease of unknown cause, and the involvement of fibroblasts in its pathogenesis is well recognized. However, a comprehensive understanding of fibroblasts’ heterogeneity, their molecular characteristics, and their clinical relevance in IPF is lacking. In this study, we aimed to systematically classify fibroblast populations, uncover the molecular and biological features of fibroblast subtypes in fibrotic lung tissue, and establish an IPF-associated, fibroblast-related predictive model for IPF. Herein, a meticulous analysis of scRNA-seq data obtained from lung tissues of both normal and IPF patients was conducted to identify fibroblast subpopulations in fibrotic lung tissues. In addition, hdWGCNA was utilized to identify co-expressed gene modules associated with IPF-related fibroblasts. Furthermore, we explored the prognostic utility of signature genes for these IPF-related fibroblast subtypes using a machine learning-based approach. Two predominant fibroblast subpopulations, termed IPF-related fibroblasts, were identified in fibrotic lung tissues. Additionally, we identified co-expressed gene modules that are closely associated with IPF-fibroblasts by utilizing hdWGCNA. We identified gene signatures that hold promise as prognostic markers in IPF. Moreover, we constructed a predictive model specifically focused on IPF-fibroblasts which can be utilized to assess disease prognosis in IPF patients. These findings have the potential to improve disease prediction and facilitate targeted interventions for patients with IPF.