Riza M. Daza

The paper introduces OPTIMuS PRIME-Del, a CRISPR-based method for multiplexed, precise deletion of cis-regulatory elements, validated through molecular tagging and scRNA-seq analysis.

The paper identifies chromatin-opening transcription factors in mouse embryonic stem cells using CRISPRa screens, revealing autonomous, opportunistic, and hierarchical mechanisms.

A prime-editing recorder in one mouse embryo yields a dated, zygote-rooted lineage tree of 1.28M cells at E13.5, enabling clonal dominance, sibling-fate and cell-type coupling analyses.

This paper introduces Shred-seq, a method to scan large-scale deletions in the human genome, revealing essential regions including noncoding sequences and establishing a lower bound for viability.

The paper develops STEAM, an evolution-augmented deep learning model, to predict mammalian enhancer landscapes across species using comparative genomics and single-cell data.

The paper studies the evolutionary changes of mammalian enhancers by analyzing orthologous sequences across multiple species and using deep learning to model functional changes.

The paper identifies active human neural enhancers and CRISPRa gRNAs that upregulate NDD risk genes, providing a resource for cis-regulatory therapy development.

The paper describes a method to generate human embryo-like structures by pre-patterning stem cells and mixing them, resulting in models with extended anterior-posterior organization and neural features.

This paper introduces Genome-Shuffle-seq, a method for generating and mapping structural variants in mammalian genomes, and demonstrates its application in mouse embryonic stem cells.

The paper describes a CRISPRa screening method combining multiplex perturbations with sc-RNA-seq to identify cell-type-specific regulatory elements and their target genes.

The paper uses single-nucleus ATAC-Seq and RNA-Seq to analyze age and sex differences in human heart cells, identifying gene expression and chromatin changes linked to these factors.

The paper introduces SCOPE, an optics-free method to reconstruct 2D and 3D shapes using DNA barcode proximity graphs from sequencing data.

This paper identifies epigenetic features affecting prime editing efficiency and shows how chromatin state can be manipulated to enhance or reduce editing efficiency at specific genomic loci.

12.4 million single-nucleus transcriptomes from 83 staged mouse embryos (E8 to P0) yield 190 cell types, a zygote-to-pup cell-type tree, and rapid postnatal transcriptional shifts.

This study induces human gastruloids with embryo-like structures using retinoic acid and shows they match E9.5 mouse embryos in developmental stage through single-cell RNA-seq and computational staging.

The paper introduces sciPlex-ATAC-seq for high-capacity multiplexing of single-cell chromatin accessibility, demonstrating its use in chemical epigenomics and immune stimulation studies.

This paper introduces prime-SGE, a prime editing framework for assessing drug resistance variants across multiple oncogenes in a single experiment.

This paper introduces a stem cell-derived human embryo model that mimics post-implantation development and identifies SOX17's inhibitory role in anterior hypoblast-like cell specification.

This paper introduces DNA Ticker Tape, a system for in vivo molecular recording that sequentially records events using CRISPR-Cas9 and prime editing, demonstrating its ability to record complex event histories and cell lineage.

The paper presents the first single cell ATAC-seq study in C. elegans, identifying tissue-specific chromatin accessibility patterns aligned with existing RNA-seq data.

TransMPRA combines CRISPR perturbation and MPRAs to identify trans-acting factors influencing enhancer activity.

The paper uses single-cell ATAC-seq and deep learning to analyze pancreatic islet cells, revealing cell-specific regulatory signatures linked to type 2 diabetes.

The paper functionally classifies thousands of BRCA1 variants using saturation genome editing to determine their impact on cancer risk.

The paper profiles chromatin accessibility in Drosophila embryos to uncover cis-regulatory programs and cell fate determinants during development.

This paper introduces THEMIS, a method for analyzing tumor heterogeneity across multiple biopsies, showing improved accuracy over existing methods and applications in tracking cancer progression and treatment resistance.

This paper introduces sci-RNA-seq, a combinatorial indexing method for single-cell RNA sequencing, and applies it to profile 42,035 C. elegans cells, identifying 27 cell types and their specific genes.

The paper introduces Cicero, a statistical method to link regulatory elements to target genes using single-cell chromatin accessibility data, and applies it to study myogenesis.