DiMeLo-seq: a long-read, single-molecule method for mapping protein-DNA interactions genome wide.

Studies of genome regulation routinely use high-throughput DNA sequencing approaches to determine where specific proteins interact with DNA, and they rely on DNA amplification and short-read sequencing, limiting their quantitative application in complex genomic regions. To address these limitations, we developed directed methylation with long-read sequencing (DiMeLo-seq), which uses antibody-tethered enzymes to methylate DNA near a target protein's binding sites in situ. These exogenous methylation marks are then detected simultaneously with endogenous CpG methylation on unamplified DNA using long-read, single-molecule sequencing technologies. We optimized and benchmarked DiMeLo-seq by mapping chromatin-binding proteins and histone modifications across the human genome. Furthermore, we identified where centromere protein A localizes within highly repetitive regions that were unmappable with short sequencing reads, and we estimated the density of centromere protein A molecules along single chromatin fibers. DiMeLo-seq is a versatile method that provides multimodal, genome-wide information for investigating protein-DNA interactions.

Mapping protein-DNA interactions with DiMeLo-seq.

We recently developed directed methylation with long-read sequencing (DiMeLo-seq) to map protein-DNA interactions genome wide. DiMeLo-seq is capable of mapping multiple interaction sites on single DNA molecules, profiling protein binding in the context of endogenous DNA methylation, identifying haplotype-specific protein-DNA interactions and mapping protein-DNA interactions in repetitive regions of the genome that are difficult to study with short-read methods. With DiMeLo-seq, adenines in the vicinity of a protein of interest are methylated in situ by tethering the Hia5 methyltransferase to an antibody using protein A. Protein-DNA interactions are then detected by direct readout of adenine methylation with long-read, single-molecule DNA sequencing platforms such as Nanopore sequencing. Here we present a detailed protocol and practical guidance for performing DiMeLo-seq. This protocol can be run on nuclei from fresh, lightly fixed or frozen cells. The protocol requires 1-2 d for performing in situ targeted methylation, 1-5 d for library preparation depending on desired fragment length and 1-3 d for Nanopore sequencing depending on desired sequencing depth. The protocol requires basic molecular biology skills and equipment, as well as access to a Nanopore sequencer. We also provide a Python package, dimelo, for analysis of DiMeLo-seq data.

Loss of TJP1 disrupts gastrulation patterning and increases differentiation toward the germ cell lineage in human pluripotent stem cells.

Biological patterning events that occur early in development establish proper tissue morphogenesis. Identifying the mechanisms that guide these patterning events is necessary in order to understand the molecular drivers of development and disease and to build tissues in vitro. In this study, we use an in vitro model of gastrulation to study the role of tight junctions and apical/basolateral polarity in modulating bone morphogenic protein-4 (BMP4) signaling and gastrulation-associated patterning in colonies of human pluripotent stem cells (hPSCs). Disrupting tight junctions via knockdown (KD) of the scaffolding tight junction protein-1 (TJP1, also known as ZO1) allows BMP4 to robustly and ubiquitously activate pSMAD1/5 signaling over time, resulting in loss of the patterning phenotype and marked differentiation bias of pluripotent stem cells to primordial germ cell-like cells (PGCLCs). These findings give important insights into how signaling events are regulated and lead to spatial emergence of diverse cell types in vitro.

Mechanical phenotyping reveals unique biomechanical responses in retinoic acid-resistant acute promyelocytic leukemia.

All-trans retinoic acid (ATRA) is an essential therapy in the treatment of acute promyelocytic leukemia (APL), but nearly 20% of patients with APL are resistant to ATRA. As there are no biomarkers for ATRA resistance that yet exist, we investigated whether cell mechanics could be associated with this pathological phenotype. Using mechano-node-pore sensing, a single-cell mechanical phenotyping platform, and patient-derived APL cell lines, we discovered that ATRA-resistant APL cells are less mechanically pliable. By investigating how different subcellular components of APL cells contribute to whole-cell mechanical phenotype, we determined that nuclear mechanics strongly influence an APL cell's mechanical response. Moreover, decondensing chromatin with trichostatin A is especially effective in softening ATRA-resistant APL cells. RNA-seq allowed us to compare the transcriptomic differences between ATRA-resistant and ATRA-responsive APL cells and highlighted gene expression changes that could be associated with mechanical changes. Overall, we have demonstrated the potential of "physical" biomarkers in identifying APL resistance.

µDamID: A Microfluidic Approach for Joint Imaging and Sequencing of Protein-DNA Interactions in Single Cells.

DNA adenine methyltransferase identification (DamID) measures a protein's DNA-binding history by methylating adenine bases near each protein-DNA interaction site and then selectively amplifying and sequencing these methylated regions. Additionally, these interactions can be visualized using m6A-Tracer, a fluorescent protein that binds to methyladenines. Here, we combine these imaging and sequencing technologies in an integrated microfluidic platform (µDamID) that enables single-cell isolation, imaging, and sorting, followed by DamID. We use µDamID and an improved m6A-Tracer protein to generate paired imaging and sequencing data from individual human cells. We validate interactions between Lamin-B1 protein and lamina-associated domains (LADs), observe variable 3D chromatin organization and broad gene regulation patterns, and jointly measure single-cell heterogeneity in Dam expression and background methylation. µDamID provides the unique ability to compare paired imaging and sequencing data for each cell and between cells, enabling the joint analysis of the nuclear localization, sequence identity, and variability of protein-DNA interactions. A record of this paper's transparent peer review process is included in the Supplemental Information.