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A set of 20 computational metrics was evaluated to determine whether they could predict the functionality of synthetic enzyme sequences produced by generative protein models, resulting in the development of a computational filter, COMPSS, that increased experimental success rates by 50–150%, tested in over 500 natural and AI-generated enzymes.
Borderlands Science is a casual mini-game released within a mass-market video game that crowdsources the alignment of one million RNA sequences from the human microbiome. In 3 years, 4 million participants generated over 135 million puzzle solutions that were used to build a reference alignment and improve microbial phylogeny.
Using synthetic biology, we engineered a cellulose-producing bacterium that can produce eumelanin and respond to light, so that it is possible to grow a microbial leather material that is colored black or contains projected black patterns.
Cells interact with their local environment to enact global tissue function. By harnessing gene–gene covariation in cellular neighborhoods from spatial transcriptomics data, the covariance environment (COVET) niche representation and the environmental variational inference (ENVI) data integration method model phenotype–microenvironment interplay and reconstruct the spatial context of dissociated single-cell RNA sequencing datasets.
The underrepresentation of functional glial cells is a major challenge in brain organoid models. We developed an astroglia-enriched cortical organoid model that allows efficient generation of functional astrocytes and enables the formation of astroglial morphological subclasses with layer-specific gene expression profiles upon transplantation into the mouse brain.
The Iniquitate pipeline assessed the impacts of cell-type imbalance on single-cell RNA sequencing integration through perturbations to dataset balance. The results indicated that cell-type imbalance not only leads to loss of biological signal in the integrated space, but also can change the interpretation of downstream analyses after integration.
We developed a deep learning-based method, EMRNA, to automatically model RNA structures from cryo-electron microscopy maps. Evaluation of EMRNA on diverse test sets of RNA maps shows that it builds RNA models with high accuracy and efficiency.
We developed the OMArk software package for evaluating protein-coding gene annotation quality. In addition to assessing the completeness of a proteome, OMArk estimates the overall quality of the gene set’s content, a feature that will help to improve public protein sequence data.
By applying the logic of conditional enzymes, we have developed a zinc-finger-dependent recombinase system, the editing activity of which is induced by zinc finger DNA binding. The system combines the precision of recombinases with the DNA target site programmability of zinc finger domains.
Many questions on the activity of the Ras proto-oncogene are unanswered due to the lack of tools for detecting active Ras in living cells. Here, we used protein design and structure prediction algorithms to develop biosensors that detect the activity and environment of endogenous Ras.
Mapping higher-order RNA structures and intermolecular RNA–RNA interactions throughout the transcriptome is critical for understanding RNA functions. We developed KARR-seq, a chemical-assisted RNA proximity capture and sequencing technology that enables sensitive and accurate detection of the RNA structurome and functional RNA–RNA interactions.
Expression profiles of single live cells are generated from Raman microscopy using deep learning, enabling us to track expression dynamics along cell reprogramming or differentiation.
We developed synthetic serum markers that can be expressed in the brain but are transported into the blood, enabling minimally invasive monitoring of gene expression in the brain with a simple blood test.
Repetitive DNA sequences known as tandem repeats (TRs) are linked to dozens of monogenic diseases and to cancer. We developed a computational method to characterize TRs using PacBio HiFi sequencing. This software can be used to discover and profile pathogenic repeat expansions and to catalog genetic and epigenetic variation in TR regions.
Chimeric antigen receptor (CAR) T cells in the solid tumor microenvironment enter a partially dysfunctional state called T cell exhaustion. Interleukin (IL)-10-producing CAR T cells retain their metabolic fitness, resist T cell exhaustion and display unprecedented antitumor activity indicated by high cure rates and durable responses in mice. Clinical studies of IL-10-producing CAR T cells are underway.
pA regulator is an RNA-based, non-immunogenic regulation system of gene expression that achieves up to 900-fold induction by using an inducer drug within the clinically safe dose range.
We developed luciferase-based splicing reporters to evaluate 718 RNA-binding proteins (RBPs) for the ability to activate exon inclusion. Our screen detected RBPs with no previously characterized roles in splicing and utilized RBP domains displaying potent exon activation to develop programmable splicing modulators with improved strength, generalizability and size over previous versions.
The protracted timeline of maturation is a major bottleneck in generating adult-like neurons from human pluripotent stem cells. We identify a combination of four chemicals that promotes neuronal maturation by repressing epigenetic factors that inhibit it and stimulating activity-dependent factors that promote it.
Thousands of cellular mRNAs contain the methyl modification m6A, which plays critical roles in mRNA processing and gene expression. We developed GEMS, a genetically encoded m6A sensor that provides a simple readout for m6A methylation in living cells and offers a platform for achieving m6A-coupled effector protein expression.
Using an experimental and computational framework inspired by compressed sensing, we greatly reduced the number of measurements needed to run Perturb-seq. Our compressed Perturb-seq strategy relies on collecting measurements comprising random linear combinations of genetic perturbations, followed by deconvolving the perturbation effects on the transcriptome using sparsity-exploiting algorithms.
