Lacramioara Bintu

This paper introduces a method to measure RNA structure and protein binding in human cells, showing how RNA folding and sequence determine protein occupancy and function.

This paper introduces Swap-seq, a method to systematically delete and quantify the effects of enhancers, silencers, and topological elements on gene expression in human cells.

This study introduces match-seq, a method to trace cell-cell interactions in vivo using CRISPR screens, revealing genetic regulators of tumor-immune interactions.

The paper shows that single-cell gene expression responses in mammalian systems are all-or-none due to stable epigenetic variability, not bursty transcription or bistability.

The paper identifies immunomodulatory bacterial microproteins from Leptotrichia using a microproteome screen and shows they influence macrophage polarization.

The paper investigates chromatin state transitions during epigenetic memory formation by analyzing single-cell gene expression and chromatin structure changes after repressor recruitment and release.

This paper identifies bifunctional transcriptional effector domains that generate gene expression pulses by switching from activation to repression based on DNA occupancy, and develops a kinetic model to program cell states.

The paper presents a workflow using machine learning to optimize protein function and reduce immunogenicity in cell and gene therapies.

The paper develops HT-recruit to measure transcriptional effector function across contexts and identifies context-dependent effectors for CRISPR-based transcription control.

The paper develops a method to generate functional nanobodies for gene regulation and epigenetic control in human cells, identifying tens of effective nanobodies.

This paper introduces an IFFL-based CRISPR-Cas system to reduce gene expression variability, demonstrating improved dosage control for RAI1 in a mouse model.

The paper predicts and designs transcriptional repressor domains using mutational scans and a deep learning model called TENet, identifying functional variants and enabling synthetic design.

This study quantifies direct interactions between 204 human activation domains and eight co-activators using a microfluidic platform, revealing 334 novel binding pairs and demonstrating the utility of analytical models for predicting activation.

The paper identifies hundreds of RNA-regulatory effectors in human RBPs using HT-RNA-Recruit and develops a synthetic RNA regulator with a predictive mathematical model.

This study links transcription factor binding to gene expression by analyzing chromatin configurations using single-molecule footprinting in human cells.

The paper measures how combinations of transcriptional activators and repressors affect gene expression and uses this to create a tunable synthetic transcription factor.

The paper identifies and characterizes viral transcriptional effector domains from coronaviruses and herpesviruses using high-throughput assays and perturbations.

The paper systematically maps and characterizes transcriptional effector domains in human chromatin regulators and transcription factors, identifying new activation and repression domains and their functional features.

The paper studies how gene silencing and reactivation spread between neighboring genes in single cells, finding different dynamics based on chromatin regulators and insulator elements.

The paper introduces a method to profile off-target specificity of CRISPR enzymes using ssDNA mapping, applicable to both catalytically dead and active Cas9 variants.

This paper develops nanobodies to control gene expression and epigenetic memory by recruiting chromatin regulators to specific genomic locations.

The paper develops HT-recruit, a high-throughput assay to identify transcriptional effectors in human proteins, finding repressor function correlates with evolutionary age and discovering new activator domains.

The paper identifies off-target effects in CRISPR-Cas9 screens for non-coding elements and shows that strict filtering with specificity scores improves the identification of essential regulatory regions.