Barcoding microbial ribosomal RNA creates a recording of gene transfer events without requiring translation.

Primary T cells with multiple genetic modifications are rapidly isolated by negative selection.

Recent clinical trials testing CAR T cells on solid tumors have yielded promising outcomes that can be enhanced with further CAR engineering.

CRISPR–Cas enzymes must recognize a protospacer-adjacent motif (PAM) to edit a genomic site, greatly limiting the range of targetable sequences in a genome. Although engineering strategies to alter PAM specificity exist, they typically require labor-intensive, iterative experimentation. We introduce an evolution-informed deep learning model, Protein2PAM, to efficiently guide the design of Cas protein variants tailored to recognize specific PAMs. Trained on a dataset of over 45,000 CRISPR–Cas PAMs, Protein2PAM rapidly and accurately predicts PAM specificity directly from Cas proteins across type I, II and V CRISPR–Cas systems. Using in silico mutagenesis, the model identifies residues critical for PAM recognition in Cas9 without using structural information. We use Protein2PAM to computationally evolve Nme1Cas9, generating variants with broadened PAM recognition and up to a 50-fold increase in PAM cleavage rates compared to the wild type in vitro. Our machine learning approach allows Ca

CAR T cells engineered to express IL-10 eradicate solid tumors and metastases.

A new class of biotech buyer is emerging, as frontier AI and big tech companies acquire early-stage biology foundational platforms. How do big tech acquisitions differ from traditional pharma exits, a...

DRAGEN rapidly identifies diverse types of genetic variants.