Overcoming lentiviral delivery limitations in hard-to-transduce suspension cells for genome-wide CRISPR screening
Lentiviral vectors are a cornerstone delivery modality of biomedical research, renowned for their ability to stably integrate genetic material into the host genome, enabling sustained transgene expression and long-term genetic manipulation. These properties make them indispensable tools in functional genomics and genome engineering, particularly for delivering molecular components in high-throughput CRISPR screening, a powerful approach for uncovering the genetic basis of complex cellular mechanisms and phenotypes. However, challenges such as lentiviral-induced recombination, unpredictable integration profiles, and variable susceptibility of target cells to transduction can introduce noise and compromise experimental outcomes. In this study, we selected two suspension-adapted mammalian cell lines, Chinese Hamster Ovary cells CHO-K1 and Human Embryonic Kidney cells HEK293-6E, due to their widespread use in recombinant protein production. Recognizing the influence of intrinsic cell line properties and transduction methodology, we compared two distinct procedures: spinoculation and static transduction. By implementing a two-step static transduction protocol, we achieved significantly higher transduction efficiencies while minimizing cellular stress, streamlining workflows, and eliminating scalability limitations inherent to large-scale lentiviral applications like genome-wide CRISPR screens. To further characterize the variation in lentiviral integration, we used droplet digital PCR (ddPCR) to quantify copy number variation (CNV) both at the pooled population level and within individual clonal isolates. This comprehensive analysis underscores the robustness of our optimized protocol in enhancing transduction efficiency in difficult-to-transduce suspension cell lines. It further emphasizes the importance of carefully modulating infection rates to limit multiple integrations, ensuring the accuracy and consistency required for large-scale functional genomics applications.