Biology subjects

Mok, A.

Publications and source records attributed to Mok, A..

6 recordsLinked to original sources

bioRxiv2026

Engineered Allosteric Control Enhances Specificity and Potency of CRISPR-CasX-based Epigenetic Repressors

Epigenetic silencers are targeted therapeutics capable of persistently silencing genes without altering DNA sequence. However, many current silencers rely on direct fusion of a constitutively active DNA methyltransferase catalytic domain, that may cause off-target DNA methylation and cellular toxicity. In contrast, endogenous DNA methyltransferases contain a regulatory domain (ADD) that requires recognition of a permissive histone state before DNA methylation can occur. We reinstate this sequential proofreading logic in a compact CasX-based epigenetic repressor, generating Epigenetic Long-term CasX Repressors (ELXRs) that read local chromatin state before writing DNA methylation. In ELXRs, a repressor domain converts active chromatin into a state that unlocks the allosteric DNA methyltransferase. This creates a sequential, multi-gated process that preferentially restricts DNA methylation to the target site. Surprisingly, implementing this proofreading mechanism improved not only specificity, but also activity. ELXRs decreased off-target methylation up to 10-fold and rescued DNMT3A-dependent growth defects, with transcriptome profiling showing up to 150-fold fewer dysregulated genes, while also increasing on-target repression up to 4-fold across multiple loci. In mouse models, lipid nanoparticle delivery produced potent, durable PCSK9 silencing with precise promoter methylation. Together, these results demonstrate that coupling molecular recognition to effector activity through sequential proofreading can simultaneously improve specificity and potency.

molecular biology↗

bioRxiv2023

A Large Field-of-View Two- and Three-Photon Microscope for High-Resolution Deep Tissue Imaging

In vivo imaging of large-scale neuron activity plays a pivotal role in unraveling the function of the brains network. Multiphoton microscopy, a powerful tool for deep-tissue imaging, has received sustained interest in advancing its speed, field of view and imaging depth. However, to avoid thermal damage in scattering biological tissue, field of view decreases exponentially as imaging depth increases. We present a suite of innovations to overcome constraints on the field of view in three-photon microscopy and to perform deep imaging that is inaccessible to two-photon microscopy. These innovations enable us to image neuronal activities in a [~]3.5-mm diameter field-of-view at 4 Hz with single-cell resolution and in the deepest cortical layer of mouse brains. We further demonstrate simultaneous large field-of-view two-photon and three-photon imaging, subcortical imaging in the mouse brain, and whole-brain imaging in adult zebrafish. The demonstrated techniques can be integrated into any multiphoton microscope for large-field-of-view and system-level neural circuit research.

neuroscience↗

bioRxiv2023

choros: correction of sequence-based biases for accurate quantification of ribosome profiling data

Ribosome profiling quantifies translation genome-wide by sequencing ribosome-protected fragments, or footprints. Its single-codon resolution allows identification of translation regulation, such as ribosome stalls or pauses, on individual genes. However, enzyme preferences during library preparation lead to pervasive sequence artifacts that obscure translation dynamics. Widespread over- and under-representation of ribosome footprints can dominate local footprint densities and skew estimates of elongation rates by up to five fold. To address these biases and uncover true patterns of translation, we present choros, a computational method that models ribosome footprint distributions to provide bias-corrected footprint counts. choros uses negative binomial regression to accurately estimate two sets of parameters: (i) biological contributions from codon-specific translation elongation rates; and (ii) technical contributions from nuclease digestion and ligation efficiencies. We use these parameter estimates to generate bias correction factors that eliminate sequence artifacts. Applying choros to multiple ribosome profiling datasets, we are able to accurately quantify and attenuate ligation biases to provide more faithful measurements of ribosome distribution. We show that a pattern interpreted as pervasive ribosome pausing near the beginning of coding regions is likely to arise from technical biases. Incorporating choros into standard analysis pipelines will improve biological discovery from measurements of translation.

bioinformatics↗

bioRxiv2023

Streamlined and sensitive mono- and diribosome profiling in yeast and human cells

Ribosome profiling has unveiled diverse regulations and perturbations of translation through a transcriptome-wide survey of ribosome occupancy, read out by sequencing of ribosome-protected mRNA fragments. Generation of ribosome footprints and their conversion into sequencing libraries is technically demanding and sensitive to biases that distort the representation of physiological ribosome occupancy. We address these challenges by producing ribosome footprints with P1 nuclease rather than RNase I and replacing RNA ligation with Ordered Two-Template Relay, a single-tube protocol for sequencing library preparation that incorporates adapters by reverse transcription. Our streamlined approach reduced sequence bias and enhanced enrichment of ribosome footprints relative to ribosomal RNA. Furthermore, P1 nuclease preserved a myriad of distinct juxtaposed ribosome complexes informative about yeast and human ribosome fates during translation initiation, stalling, and termination. Our optimized methods for mRNA footprint generation and capture provides a richer translatome profile using lower input and fewer technical challenges.

molecular biology↗

bioRxiv2021

riboviz 2: A flexible and robust ribosome profiling data analysis and visualization workflow

MotivationRibosome profiling, or Ribo-seq, is the state of the art method for quantifying protein synthesis in living cells. Computational analysis of Ribo-seq data remains challenging due to the complexity of the procedure, as well as variations introduced for specific organisms or specialized analyses. Many bioinformatic pipelines have been developed, but these pipelines have key limitations in terms of functionality or usability. ResultsWe present riboviz 2, an updated riboviz package, for the comprehensive transcript-centric analysis and visualization of Ribo-seq data. riboviz 2 includes an analysis workflow built on the Nextflow workflow management system, combining freely available software with custom code. The package is extensively documented and provides example configuration files for organisms spanning the domains of life. riboviz 2 is distinguished by clear separation of concerns between annotation and analysis: prior to a run, the user chooses a transcriptome in FASTA format, paired with annotation for the CDS locations in GFF3 format. The user is empowered to choose the relevant transcriptome for their biological question, or to run alternative analyses that address distinct questions. riboviz 2 has been extensively tested on various library preparation strategies, including multiplexed samples. riboviz 2 is flexible and uses open, documented file formats, allowing users to integrate new analyses with the pipeline. Availabilityriboviz 2 is freely available at github.com/riboviz/riboviz. Supplementary information

bioinformatics↗

bioRxiv2019

Multi-ATOM: Ultrahigh-throughput single-cell quantitative phase imaging with subcellular resolution

A growing body of evidence has substantiated the significance of quantitative phase imaging (QPI) in enabling cost-effective and label-free cellular assay, which provides useful insights into understanding biophysical properties of cells and their roles in cellular functions. However, available QPI modalities are limited by the loss of imaging resolution at high throughput and thus run short of sufficient statistical power at the single cell precision to define cell identities in a large and heterogeneous population of cells - hindering their utility in mainstream biomedicine and biology. Here we present a new QPI modality, coined multi-ATOM that captures and processes quantitative label-free single-cell images at ultra-high throughput without compromising sub-cellular resolution. We show that multi-ATOM, based upon ultrafast phase-gradient encoding, outperforms state-of-the-art QPI in permitting robust phase retrieval at a QPI throughput of >10,000 cell/sec, bypassing the need for interferometry which inevitably compromises QPI quality under ultrafast operation. We employ multi-ATOM for large-scale, label-free, multi-variate, cell-type classification (e.g. breast cancer sub-types, and leukemic cells versus peripheral blood mononuclear cells) at high accuracy (>94%). Our results suggest that multi-ATOM could empower new strategies in large-scale biophysical single-cell analysis with applications in biology and enriching disease diagnostics.

bioengineering↗