Nature biotechnology最新文献

The complex nature of the immunosuppressive tumor microenvironment (TME) requires multi-agent combinations for optimal immunotherapy. Here we describe multiplex universal combinatorial immunotherapy via gene silencing (MUCIG), which uses CRISPR–Cas13d to silence multiple endogenous immunosuppressive genes in the TME, promoting TME remodeling and enhancing antitumor immunity. MUCIG vectors targeting four genes delivered by adeno-associated virus (AAV) (Cd274/Pdl1, Lgals9/Galectin9, Lgals3/Galectin3 and Cd47; AAV-Cas13d-PGGC) demonstrate significant antitumor efficacy across multiple syngeneic tumor models, remodeling the TME by increasing CD8⁺ T-cell infiltration while reducing neutrophils. Whole transcriptome profiling validates the on-target knockdown of the four target genes and shows limited potential off-target or downstream gene alterations. AAV-Cas13d-PGGC outperforms corresponding shRNA treatments and individual gene knockdown. We further optimize MUCIG by employing high-fidelity Cas13d (hfCas13d), which similarly showed potent gene silencing and in vivo antitumor efficacy, without weight loss or liver toxicity. MUCIG represents a universal method to silence multiple immune genes in vivo in a programmable manner, offering broad efficacy across multiple tumor types.

Efficient and accurate nanocarrier development for targeted drug delivery is hindered by a lack of methods to analyze its cell-level biodistribution across whole organisms. Here we present Single Cell Precision Nanocarrier Identification (SCP-Nano), an integrated experimental and deep learning pipeline to comprehensively quantify the targeting of nanocarriers throughout the whole mouse body at single-cell resolution. SCP-Nano reveals the tissue distribution patterns of lipid nanoparticles (LNPs) after different injection routes at doses as low as 0.0005 mg kg⁻¹—far below the detection limits of conventional whole body imaging techniques. We demonstrate that intramuscularly injected LNPs carrying SARS-CoV-2 spike mRNA reach heart tissue, leading to proteome changes, suggesting immune activation and blood vessel damage. SCP-Nano generalizes to various types of nanocarriers, including liposomes, polyplexes, DNA origami and adeno-associated viruses (AAVs), revealing that an AAV2 variant transduces adipocytes throughout the body. SCP-Nano enables comprehensive three-dimensional mapping of nanocarrier distribution throughout mouse bodies with high sensitivity and should accelerate the development of precise and safe nanocarrier-based therapeutics.

Correction to: Nature Biotechnology https://doi.org/10.1038/s41587-024-02494-8, published online 3 December 2024.

Correction to: Nature Biotechnology https://doi.org/10.1038/s41587-024-02410-0, published online 14 October 2024.

Protein language models have demonstrated remarkable performance in predicting the effects of missense variants but DNA language models have not yet shown a competitive edge for complex genomes such as that of humans. This limitation is particularly evident when dealing with the vast complexity of noncoding regions that comprise approximately 98% of the human genome. To tackle this challenge, we introduce GPN-MSA (genomic pretrained network with multiple-sequence alignment), a framework that leverages whole-genome alignments across multiple species while taking only a few hours to train. Across several benchmarks on clinical databases (ClinVar, COSMIC and OMIM), experimental functional assays (deep mutational scanning and DepMap) and population genomic data (gnomAD), our model for the human genome achieves outstanding performance on deleteriousness prediction for both coding and noncoding variants. We provide precomputed scores for all ~9 billion possible single-nucleotide variants in the human genome. We anticipate that our advances in genome-wide variant effect prediction will enable more accurate rare disease diagnosis and improve rare variant burden testing.

Optical pooled screening offers a broader-scale alternative to enrichment-based perturbation screening, using fluorescence microscopy to correlate phenotypes and perturbations across single cells. Previous methods work well in large, transcriptionally active cell lines, because they rely on cytosolic detection of endogenously expressed barcoded transcripts; however, they are limited by reliable cell segmentation, cytosol size, transcriptional activity and cell density. Nuclear In-Situ Sequencing (NIS-Seq) expands this technology by creating bright sequencing signals directly from nuclear genomic DNA to screen nucleated cells at high density and high library complexity. By inserting an inverted phage promoter downstream of the single guide RNA (sgRNA), many RNA copies of the sgRNA can be generated and sequenced independently of cellular transcription. In this study, we benchmarked NIS-Seq across eight cell types from two species and performed four genome-scale optical perturbation screens, identifying key players of inflammation-related cellular pathways. Finally, we performed a small-scale pooled optical screen in primary human macrophages from blood of healthy donors and demonstrated barcode identification in lentivirally transduced human skin tissue.

Pooled genetic screening with CRISPR–Cas9 has enabled genome-wide, high-resolution mapping of genes to phenotypes, but assessing the effect of a given genetic perturbation requires evaluation of each single guide RNA (sgRNA) in hundreds of cells to counter stochastic genetic drift and obtain robust results. However, resolution is limited in complex, heterogeneous models, such as organoids or tumors transplanted into mice, because achieving sufficient representation requires impractical scaling. This is due to bottleneck effects and biological heterogeneity of cell populations. Here we introduce CRISPR-StAR, a screening method that uses internal controls generated by activating sgRNAs in only half the progeny of each cell subsequent to re-expansion of the cell clone. Our method overcomes both intrinsic and extrinsic heterogeneity as well as genetic drift in bottlenecks by generating clonal, single-cell-derived intrinsic controls. We use CRISPR-StAR to identify in-vivo-specific genetic dependencies in a genome-wide screen in mouse melanoma. Benchmarking against conventional screening demonstrates the improved data quality provided by this technology.