Biology subjects

Yachie, N.

Publications and source records attributed to Yachie, N..

At least 19 recordsLinked to original sources

bioRxiv2026

The Gp78 ubiquitin E3 ligase drives mitophagy by regulating omegasome formation

Autophagy is initiated at endoplasmic reticulum (ER)-associated omegasomes, yet how the ER contributes to mitochondrial cargo degradation during early autophagosome formation remains unclear. Gp78 is an ER-resident RING E3 ubiquitin ligase involved in ER-associated degradation (ERAD) that also promotes Parkin-independent mitophagy. However, the spatial relationship between Gp78 ubiquitin ligase activity, omegasome formation, and mitochondrial degradation has yet to be defined. Combining targeted mitochondria labeling and super-resolution STED microscopy, we show that the Gp78 ubiquitin ligase is localized to and regulates DFCP1-positive omegasome formation, where it drives mitochondrial protein breakdown. Using fluorescent mitophagy reporters and acute ivermectin-induced mitophagy, generation of mitophagy intermediates lacking mitochondrial outer membrane (TOMM20) was found to be dependent on the Gp78 RING finger as well as the BAG6-UBL4A-USP13 ubiquitin-ligase module, and independent of PINK1 and Parkin. Object-based analysis of 3D STED images further showed that both DFCP1- and LC3-positive structures associate primarily with intact mitochondrial fragments which show progressive degradation of mitochondrial content. Gp78 localizes to and promotes, via BAG6, the formation of DFCP1-positive omegasomes and mitochondrial protein degradation in these early autophagic structures. ER-derived omegasome biogenesis is therefore coupled to Gp78-dependent ubiquitin ligase activity and local degradation of mitochondrial cargo during mitophagy.

cell biology↗

bioRxiv2026

Global tree encoding of atlas-scale single-cell genomics

The rapid expansion of single-cell genomic datasets has led to the compilation of biological resources comprising hundreds of millions of cells across tissues, developmental stages, and disease states. This has underscored the need for scalable and interpretable data representations that preserve the complex relationships and multi-scale organization of cellular states, while remaining computationally tractable at atlas scale. Existing approaches based on discrete abstractions have enabled cell annotation, clustering, and trajectory inference, but are often optimized for local inference tasks and may obscure continuous cellular relationships and multi-resolution structure within complex transcriptional and other genomic landscapes. Moreover, increasing dataset sizes often require information-reduction strategies such as random downsampling, limiting the resolution of rare cell populations and heterogeneous cellular states. Here, we present MILK, a scalable computational framework that organizes high-dimensional single-cell populations into unified tree representations. Across large-scale transcriptomic atlases, MILK enables representative subsampling with preserved information, supporting the tractable application of existing algorithms for tasks including deep generative model training and foundation model benchmarking. Additionally, MILK enables holistic, multi-resolution analyses that capture global developmental trajectories, characterize disease-associated cellular perturbations across tissues, and facilitate comparison of transcriptional programs across species within a coherent hierarchical framework. Together, these results establish the hierarchical organization of biological data as a scalable and unifying representation of cellular identity, enabling integrative analysis of single-cell genomic data across diverse contexts.

genomics↗

bioRxiv2026

A cuffed CRISPR guide RNA for microRNA activity-dependent genome editing

Cells in multicellular eukaryotic systems are diverse biological units, with characteristics and functions determined by their molecular profiles. CRISPR-Cas9 genome editing has been widely used across biology to modulate gene expression and study gene function. However, there is currently no versatile and scalable method for editing a cells genome in response to endogenous cellular signals. Here, we report the engineering of a CRISPR guide RNA that efficiently confers genome editing in response to the catalytic activity of a target microRNA (miRNA) within a cell. miRNAs are short non-coding RNAs that are widely conserved across eukaryotes and can cleave their target RNA through almost perfect base pairing. In mammals, miRNAs are largely involved in development and homeostasis as well as disease progression and developmental disorders. To leverage these properties for genome editing, we developed a cuffed guide RNA (cgRNA) which is composed of a permutated order of sequence domains from the commonly used single guide RNA (sgRNA). These permutated domains were then concatenated with a miRNA target sequence, yielding a warped guide RNA that is inactive until cleaved by a complementary miRNA. We demonstrated that cgRNA enabled efficient miRNA activity-dependent genome editing in human and mouse cell lines. Biochemical and structural analyses revealed three stages of inhibition of the CRISPR genome-editing pathway for unprocessed cgRNA. Utilizing a lentiviral library of cgRNAs containing miRNA targets covering mouse genome-wide miRNAs, we identified miRNA cleavage activities and their sequence specificities in mouse embryonic stem cells and during smooth muscle cell differentiation. Furthermore, we showed that endogenous mRNA expression could be irreversibly recorded into a DNA sequence using a cgRNA targeted by a synthetic miRNA repeat. cgRNA is a simple, robust, miRNA activity-gated genome editing system that could facilitate the development of cell state-specific genome editing, the mapping of miRNA activity and gene expression landscapes, and the recording of molecularly determined cell states during the long-term progression of multicellular systems.

bioengineering↗

bioRxiv2026

DemuxHMM: Large-Scale Single-Cell Embryo Profiling via Recombination Barcoding

High-resolution developmental time-courses with single-cell RNA sequencing (scRNA-seq) increasingly target trajectory inference and other analyses in the study of development and disease [1-6]. These datasets are often generated by pooling individuals and inferring cell-to-individual mappings after sequencing, in a process called demultiplexing. Existing demultiplexing methods are limited in the number of timepoints they can support, due to either the need for individual-by-individual processing or reduced accuracy at large numbers of individuals. To address these limitations, we introduce a combined experimental and computational framework for creating large-scale, individual-resolved datasets. Our framework couples a simple breeding scheme that creates contiguous SNP patterns (recombination barcodes) with a recombination-aware demultiplexing method, DemuxHMM, that explicitly models this structure with a Hidden Markov Model (HMM). We demonstrate substantial performance and scalability gains from this combined approach on simulated data, highlighting its potential to enable the creation of large-scale single-cell time series.

bioinformatics↗

bioRxiv2025

Detecting and avoiding homology-based data leakage in genome-trained sequence models

Models that predict function from DNA sequence have become critical tools in deciphering the roles of genomic sequences and genetic variation within them. However, traditional approaches for dividing the genomic sequences into training data, used to create the model, and test data, used to determine the models performance on unseen data, fail to account for the widespread homology within genomes. Using simulations, we illustrate how homology-based data leakage can lead to overestimation of model performance. Across a variety of genomics models, we demonstrate that performance on test sequences varies systematically by their similarity with training sequences. Models generally perform well on distant sequences, reflecting the application of learned generalizable principles. At higher and intermediate similarity, models rely on memorized associations, inflating performance when function is conserved between homologs but failing when homologous sequences have functionally diverged. To dissect and mitigate these effects, we introduce hashFrag, a scalable solution for homology detection and data partitioning. Using hashFrag, we demonstrate how to create homology-aware evaluations of model performance, and improve model generalizability by providing improved splits for model training. Altogether, we establish how homology creates a systematic bias in genome-trained models and must be accounted for to ensure reliable evaluation of sequence-to-function predictors.

bioinformatics↗

bioRxiv2023

Highly efficient transgenic mouse production using piggyBac and its application to rapid phenotyping at the founder generation

Pronuclear microinjection is the most popular method for producing transgenic (Tg) animals. Because the production efficiency is typically less than 20%, phenotypic characterization of Tg animals is generally performed on the next generation (F1) onwards. However, apart from in rodents, in many animal species with long generation times, it is desirable to perform phenotyping in the founder (F0) generation. In this study, we attempted to optimize a method of Tg mouse production to achieve higher Tg production efficiency using piggyBac transposon systems and established optimal conditions under which almost all individuals in the F0 generation were Tg. We also succeeded in generating bacterial artificial chromosome Tg mice with efficiency of approximately 70%. By combining this method with genome editing technology, we established a new strategy to perform phenotyping of mice with tissue-specific knockout using the F0 generation. Taking the obtained findings together, by using this method, experimental research using Tg animals can be carried out more efficiently.

bioengineering↗

bioRxiv2023

Pooled CRISPR screening of high-content cellular phenotypes by ghost cytometry

Fast enrichment of cells based on morphological information remains a challenge, limiting genome-wide perturbation screening for diverse high-content phenotypes of cells. Here we show that multi-modal ghost cytometry-based cell sorting is applicable to fast pooled CRISPR screening for both fluorescence and label-free high-content phenotypes of millions of cells. By employing the high-content cell sorter in the fluorescence mode, we enabled the genome-wide CRISPR screening of genes that affect NF-{kappa}B nuclear translocation. Furthermore, by employing the multi-parametric, label-free mode, we performed the large-scale screening to identify a gene involved in macrophage polarization. Especially the label-free platform can enrich target phenotypes without invasive staining, preserving untouched cells for downstream assays and unlocking the potential to screen for the cellular phenotypes even when suitable markers are lacking. One-Sentence SummaryMachine vision-based cell sorter enabled genome-wide perturbation screens for high-content cell phenotypes even without labeling

cell biology↗

bioRxiv2023

A multi-kingdom genetic barcoding system for precise target clone isolation

Clonal heterogeneity underlies diverse biological processes, including cancer progression, cell differentiation, and microbial evolution. Cell tagging strategies with DNA barcodes have recently enabled analysis of clone size dynamics and clone-restricted transcriptomic landscapes of heterogeneous populations. However, isolating a target clone that displays a specific phenotype from a complex population remains challenging. Here, we present a new multi-kingdom genetic barcoding system, CloneSelect, in which a target cell clone can be triggered to express a reporter gene for isolation through barcode-specific CRISPR base editing. In CloneSelect, cells are first barcoded and propagated so their subpopulation can be subjected to a given experiment. A clone that shows a phenotype or genotype of interest at a given time can then be isolated from the initial or subsequent cell pools stored throughout the experimental timecourse. This novel CRISPR-barcode genetics platform provides many new ways of analyzing and manipulating mammalian, yeast, and bacterial systems. TeaserA multi-kingdom CRISPR-activatable barcoding system enables the precise isolation of target barcode-labeled clones from a complex cell population.

synthetic biology↗

bioRxiv2022

License to cut: Smart RNA guides for conditional control of CRISPR-Cas9

The Cas9 enzyme is a programmable endonuclease, whose target sequence is directed by a companion RNA guide. Cas9 and RNA guides have revolutionized biology, enabling facile editing of the genome in almost all organisms. Controlling where and when Cas9 and the guide operate is indispensable for many fields ranging from developmental biology to therapeutics, but it remains a challenge. Most methods focus on controlling Cas9 with physico-chemical means (which lack finesse, precision or multiplexing), or transcriptional tools (which are slow and difficult to design). Rather than directly engineering Cas9, engineering the RNA guide itself has emerged as a more general and potent way to manage the activity of Cas9. Here we report smart RNA guides that are conditionally activated by the presence of a specific RNA opener. Contrary to most previous approaches, the design affords ample freedom as spacer and the opener are independent. We demonstrate this flexibility by operating SmartGuides activated by a panel of miRNA relevant for human health, and by composing SmartGuides in Boolean logic circuits. Lastly, we test the SmartGuides in mammalian cells - validating the basics tenets of the design, but also highlighting the challenges that remain to be lifted for in-vivo operation.

bioengineering↗

bioRxiv2022

Quantitative assaying of SpCas9-NG with fluorescent reporters

The Cas9 enzyme has revolutionized biology in less than a decade. Engineering Cas9 to expand its functionality has become a major research goal, yet assaying variants of Cas9 remains a laborious task that is commonly performed with gel electrophoresis. Fluorescence assays have been reported for Cas9 but their utility for assaying variants of Cas9 has not been investigated in detail. Here we use a simple fluorescent assay to resolve differences of activity between the wild type Streptococcus pyogenes Cas9 (SpCas9) and SpCas9-NG, a variant with an expanded PAM repertoire. We compare the kinetics of the two enzymes on dozens of mutated RNA guides - highlighting the benefits of fluorescence such as quantitativity, sensitivity, multiplexing, non-invasiveness and real-timeness. This validates fluorescence as a tool for engineering Cas9 and lays the groundwork for directly evolving Cas9 in microfluidic compartments.

synthetic biology↗

bioRxiv2022

A universal sequencing read interpreter

Massively parallel DNA sequencing has led to the rapid growth of highly multiplexed experiments in biology. Such experiments produce unique sequencing results that require specific analysis pipelines to decode highly structured reads. However, no versatile framework that interprets sequencing reads for downstream biological analysis has been developed. Here we report INTERSTELLAR (interpretation, scalable transformation, and emulation of large-scale sequencing reads) that extracts data values encoded in theoretically any type of sequencing read and translates them into sequencing reads of any structure of choice. INTERSTELLAR first identifies sequence segments encoded in reads according to the users definition. Followed by error correction, values are extracted and compressed into an optimal space, which can be efficiently translated into sequencing reads of another structure. We demonstrated that INTERSTELLAR successfully extracted information from a range of sequencing reads and translated those of single-cell (sc)RNA-seq, scATAC-seq, and spatial transcriptomics to be analyzed by different software tools that have been developed for conceptually the same types of experiments. INTERSTELLAR will greatly facilitate the development of new sequencing-based experiments and sharing of data analysis pipelines.

bioinformatics↗

bioRxiv2022

The DNA-based global positioning system--a theoretical framework for large-scale spatial genomics

We present GPS-seq, a theoretical framework that enables massively scalable, optics-free spatial transcriptomics. GPS-seq combines data from high-throughput sequencing with manifold learning to obtain the spatial transcriptomic landscape of a given tissue section without optical microscopy. In this framework, similar to technologies like Slide-seq and 10X Visium, tissue samples are stamped on a surface of randomly-distributed DNA-barcoded spots (or beads). The transcriptomic sequences of proximal cells are fused to DNA barcodes, enabling the recovery of a transcriptomic pixel image by high-throughput sequencing. The barcode spots serve as "anchors" which also capture spatially diffused "satellite" barcodes, and therefore allow computational reconstruction of spot positions without optical sequencing or depositing barcodes to pre-specified positions. In theory, it could generate 100 mm x 100 mm spatial transcriptomic images with 10-20 m resolution by localizing 108 DNA-barcoded pixels with a single Illumina NovaSeq run. The general framework of GPS-seq is also compatible with standard single-cell (or single-nucleus) capture methods, and any modality of single-cell genomics, such as sci-ATAC-seq, could be transformed into spatial genomics in this strategy. We envision that GPS-seq will lead to breakthrough discoveries in diverse areas of biology by enabling organ-scale imaging of multiple genomic statuses at single-cell resolution for the first time.

bioinformatics↗

bioRxiv2021

BFG-PCA: tools and resources that expand the potential for binary protein interaction discovery

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein-protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, Dihydrofolate Reductase Protein-Fragment Complementation Assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11,000 protein pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

systems biology↗

bioRxiv2019

A single CRISPR base editor to induce simultaneous C-to-T and A-to-G mutations

While several Cas9-derived base editors have been developed to induce either C-to-T or A-to-G mutation at target genomic sites, the possible genome editing space when using the current base editors remains limited. Here, we present a novel base editor, Target-ACE, which integrates the abilities of both of the previously developed C-to-T and A-to-G base editors by fusing an activation-induced cytidine deaminase (AID) and an engineered tRNA adenosine deaminase (TadA) to a catalytically impaired Streptococcus pyogenes Cas9. In mammalian cells, Target-ACE enabled heterologous editing of multiple bases in a small sequence window of target sites with increased efficiency compared with a mixture of two relevant base editor enzymes, each of which may block the same target DNA molecule from the other. Furthermore, by modeling editing patterns using deep sequencing data, the editing spectra of Target-ACE and other base editors were simulated across the human genome, demonstrating the highest potency of Target-ACE to edit amino acid coding patterns. Taking these findings together, Target-ACE is a new tool that broadens the capabilities for base editing for various applications.

bioengineering↗

bioRxiv2019

Systematic perturbation of yeast essential genes using base editing

Base editors derived from CRISPR-Cas9 systems and DNA editing enzymes offer an unprecedented opportunity for the precise modification of genes, but have yet to be used at a genome-scale throughput. Here, we test the ability of an editor based on a cytidine deaminase, the Target-AID base editor, to systematically modify genes genome-wide using the set of yeast essential genes. We tested the effect of mutating around 17,000 individual sites in parallel across more than 1,500 genes in a single experiment. We identified over 1,100 sites at which mutations have a significant impact on fitness. Using previously determined and preferred Target-AID mutational outcomes, we predicted the protein variants caused by each of these gRNAs. We found that gRNAs with significant effects on fitness are enriched in variants predicted to be deleterious by independent methods based on site conservation and predicted protein destabilization. Finally, we identify key features to design effective gRNAs in the context of base editing. Our results show that base editing is a powerful tool to identify key amino acid residues at the scale of proteomes.

synthetic biology↗

bioRxiv2019

The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs

Many paralogs derive from the duplication of genes encoding homomeric proteins. Such duplication events lead to the formation of homomers and heteromers, thus creating new structures from a single event. We exhaustively characterize this phenomenon using the budding yeast protein-protein interaction network. We observe that paralogs that heteromerize are very frequent and less functionally diverged than those that lost this property, raising the possibility that heteromerization prevents functional divergence. Using in silico evolution, we show that for homomers and heteromers that share binding interfaces, mutations on one complex have pleiotropic effects on the other complex, resulting in highly correlated responses to selection. As a result, heteromerization could be preserved indirectly due to selection for the maintenance of homomers. By integrating data on gene expression and protein localization, we find that regulatory evolution could play a role in overcoming these structural pleiotropic effects and in allowing paralog functional divergence.

evolutionary biology↗

bioRxiv2018

beditor: A computational workflow for designing libraries of guide RNAs for CRISPR base editing

CRISPR-mediated base editors have opened unique avenues for scar-free genome-wide mutagenesis. Here, we describe a comprehensive computational workflow called beditor that can be broadly adapted for designing guide RNA libraries with a range of CRISPR-mediated base editors, PAM recognition sequences and genomes of many species. Additionally, in order to assist users in selecting the best sets of guide RNAs for their experiments, a priori estimates, called beditor scores are calculated. These beditor scores are intended to select guide RNAs that conform to requirements for optimal base editing: editable base falls within maximum activity window of the CRISPR-mediated base editor and produces non-confounding mutational effects with minimal off-target effects. We demonstrate the utility of the software by designing guide RNAs for base-editing mediated edition of thousands of clinically important human disease mutations.

synthetic biology↗

bioRxiv2018

Fast and global detection of periodic sequence repeats in large genomic resources

Periodically repeating DNA and protein elements are involved in various important biological events including genomic evolution, gene regulation, protein complex formation, and immunity. Notably, the currently used genome editing tools such as ZFNs, TALENs, and CRISPRs are also all associated with periodically repeating biomolecules of natural organisms. Despite the biological importance of periodically repeating sequences and the expectation that new genome editing modules could be discovered from such periodical repeats, no software that globally detects such structured elements in large genomic resources in a high-throughput and unsupervised manner has been developed. Here, we developed new software, SPADE (Search for Patterned DNA Elements), that exhaustively explores periodic DNA and protein repeats from large-scale genomic datasets based on k-mer periodicity evaluation. SPADE precisely captured reported genome-editing-associated sequences and other protein families involving repeating domains with significantly better performance than the other software designed for limited sets of repetitive biomolecular sequences.

bioinformatics↗