Biology subjects

Chadly, D. M.

Publications and source records attributed to Chadly, D. M..

4 recordsLinked to original sources

bioRxiv2026

Regenerative base editing enables deep lineage recording

Reconstructing the lineage histories of individual cells can reveal the dynamics of developmental and disease processes. In engineered recording systems, cells stochastically edit synthetic barcode sequences as they proliferate, creating distinct, heritable edit patterns that can be used to reconstruct the lineage trees relating individual cells in a manner analogous to phylogenetic reconstruction. However, recording depth is often limited by the kinetics of the editing process: the rate of editing declines exponentially over time for an array of independently editable targets, leading to most edits occurring in early generations. Here, we introduce the hypercascade, a regenerative molecular recording system that takes advantage of the predictability of A-to-G base editing to progressively create new target sites over time. The hypercascade packs 4 editable target sites in every 20 bp of sequence, enabling high density information storage. More importantly, the hypercascades regenerative logic leads to an approximately constant rate of mutation accumulation over time. This in turn facilitates reconstruction of deep lineage relationships. We demonstrate this by reconstructing trees spanning 23 days of editing and approximately 17 generations after a single polyclonal engineering step. Finally, simulations show that the hypercascade has the potential to record chromatin state transition dynamics across multiple genomic loci in parallel. The hypercascade thus provides a flexible and broadly useful tool for molecular recording.

bioengineering↗

bioRxiv2025

Synaptic MEMOIR: mapping individual synapses of neurons with protein barcodes

Obtaining wiring diagrams of brains has been a major achievement for neuroscience. However, an underlying challenge in connectomics is the fundamental tradeoff between the imaging resolution needed to resolve synapses and the volume of the brain that can be imaged. For example, electron microscopy (EM) visualizes synaptic sites with ~5 nm resolution, but is difficult to scale beyond volumes of 1 mm3. Here, we present Synaptic MEMOIR (Memory with Engineered Mutagenesis with Optical in situ Readout) that enables imaging of neuronal projections in animal brains with single-synapse resolution. Synaptic MEMOIR is built around three key design features. First, protein barcodes are transported to synapses to allow matching of synaptic barcodes to cell body barcodes without high resolution imaging and the error-prone process of tracing neuronal processes across long distances. Second, Synaptic MEMOIR uses continuous mutagenesis to generate a large diversity of barcodes to uniquely label cell bodies and synapses. Last, the timing of recombination and transport can be tuned to record synaptic age or projection information. Combining these features, we demonstrated projection mapping of 113 neurons in the Drosophila melanogaster optic lobe in a volume of 9.5 million m3. Because synapses are identified by transported barcodes with optical microscopy at 300 nm resolution, this approach can potentially scale to much larger volumes, similar at least to those imaged in recent mouse brain transcriptomics atlases. In addition, synaptic MEMOIR can match barcodes across brain sections, and does not require tissue clearing to track long-range projections. Together, these results provide the foundation for a scalable optical image-based system for reconstructing the neural wiring diagrams of brains across different developmental stages, genetic backgrounds and perturbations. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=46 SRC="FIGDIR/small/690442v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@8363cdorg.highwire.dtl.DTLVardef@d3240org.highwire.dtl.DTLVardef@5e2793org.highwire.dtl.DTLVardef@1784f2a_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Genome-wide chromatin recording resolves dynamic cell state changes

Understanding how the chromatin state of a cell influences its future behavior is a major challenge throughout biology. However, most chromatin profiling methods are limited to endpoint assays. Here, we present LagTag, a method for recovery of earlier and endpoint chromatin states in the same mammalian cells. In this approach, transient expression of bacterial adenine methyltransferase fusions records the DNA binding profiles of chromatin-associated proteins of interest at earlier timepoints. Subsequent tagmentation and sequencing recovers the earlier chromatin profile from adenine methylation profiles, alongside endpoint profiles of endogenous chromatin-associated proteins. We verified that LagTag profiles aligned with those from established methods in mouse and human cells. We then applied LagTag to record and recover dynamic chromatin state transitions during mouse embryonic stem cell differentiation, capturing transcriptional signatures from pre- and post-differentiation timepoints within the same cell population. LagTag thus provides a foundation for temporally resolved chromatin profiling.

genomics↗

bioRxiv2024

Reconstructing cell histories in space with image-readable base editor recording

Knowing the ancestral states and lineage relationships of individual cells could unravel the dynamic programs underlying development. Engineering cells to actively record information within their own genomic DNA could reveal these histories, but existing recording systems have limited information capacity or disrupt spatial context. Here, we introduce baseMEMOIR, which combines base editing, sequential hybridization imaging, and Bayesian inference to allow reconstruction of high-resolution cell lineage trees and cell state dynamics while preserving spatial organization. BaseMEMOIR stochastically and irreversibly edits engineered dinucleotides to one of three alternative image-readable states. By genomically integrating arrays of editable dinucleotides, we constructed an embryonic stem cell line with 792 bits of recordable, image-readable memory, a 50-fold increase over the state of the art. Simulations showed that this memory size was sufficient for accurate reconstruction of deep lineage trees. Experimentally, baseMEMOIR allowed precise reconstruction of lineage trees 6 or more generations deep in embryonic stem cell colonies. Further, it also allowed inference of ancestral cell states and their quantitative cell state transition rates, all from endpoint images. baseMEMOIR thus provides a scalable framework for reconstructing single cell histories in spatially organized multicellular systems.

synthetic biology↗