Biology subjects

Hakozaki, H.

Publications and source records attributed to Hakozaki, H..

9 recordsLinked to original sources

bioRxiv2026

The COX2-PGE2-PKA Axis Suppresses Antiviral Immunity by Inhibiting mtDNA-Dependent STING Activation.

The innate immune cGAS-STING pathway is activated by cytosolic double-stranded DNA (dsDNA) to induce type I interferon (IFN) response, which is essential for mounting the antiviral response. However, STING activation during viral infection is often insufficient to achieve complete viral clearance, suggesting the existence of additional mechanisms that evade its activity. Here, we identified COX2/PGE2 as a negative regulator of STING activation, particularly in response to arising cytosolic mitochondrial DNA (mtDNA) generated during HSV-1 infection. Mechanistically, PGE2, through the EP4-cAMP-PKA axis, induces mitophagy to remove defective mitochondria and hence prevent the accumulation of immunostimulatory cytosolic mtDNA, thereby dampening STING-mediated type I IFN and antiviral response. Furthermore, we identified STOML2 as a downstream target of PKA that connects mitochondrial quality control with the regulation of innate immune signaling. Together, our findings establish the COX2/PGE2/PKA axis as a negative regulator of mtDNA-STING signaling that may be targeted to potentiate STING-mediated type I IFN and innate immunity. O_FIG O_LINKSMALLFIG WIDTH=166 HEIGHT=200 SRC="FIGDIR/small/716411v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1c39d56org.highwire.dtl.DTLVardef@18e7c0eorg.highwire.dtl.DTLVardef@172b353org.highwire.dtl.DTLVardef@15b215a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

cell biology↗

bioRxiv2025

Whole-cell particle-based digital twin simulations from 4D lattice light-sheet microscopy data

We present a framework for performing whole-cell digital twin simulations which integrates 4D (x,y,z,t) lattice light-sheet microscopy (LLSM) data with particle-based reaction-diffusion modeling to capture intracellular organelle dynamics. Using imaging data from Cal27 cells, we construct digital twins that incorporate mitochondrial networks, microtubule networks, dynein and kinesin motors, the plasma membrane, and the nucleus. Passive diffusive mitochondrial dynamics are parameterized using stochastic reaction-diffusion simulations in ReaDDy, while active transport is modeled explicitly by incorporating motor-driven transport along a diffusing, polarized microtubule network. Our simulations accurately reproduce experimentally observed mitochondrial dynamics across pharmacological microtubule depolymerization conditions and reproduce the mitochondrial response to intermediate perturbations without explicit re-parameterization. This novel meso-scale digital twin framework offers a bridge between atomic-scale whole-cell simulations and experimental time and length scales.

cell biology↗

bioRxiv2025

4D mitochondrial network assumes distinct and predictive phenotypes through human lung and intestinal epithelial development

Mitochondria form a dynamic three-dimensional network within the cell that exhibits a wide range of morphologies and behaviors. Depending on cell state, cell type, and cell fate, a cells mitochondrial phenotype might range from relatively isolated mitochondrial segments to complex branching networks, and from stationary mitochondria to highly motile structures. While isolated mitochondrial phenotypes have been described for a subset of cell states, types, and fates, an integrated map of how mitochondrial phenotypes change over the full course of tissue development has so far been lacking. Here, we identify the mitochondrial phenotypes that appear throughout the course of lung and intestinal epithelial development from stem cells to differentiated tissue. Using human stem cell-derived intestinal and branching lung organoids that mimic developing human organs as model systems, we extract and analyze key mitochondrial biophysical phenotypes in human development. To achieve this, we employ lattice light-sheet microscopy (LLSM), which enables high-resolution, 4D (x, y, z, time) imaging of mitochondria in organoid tissues with minimal damage to the sample. We image at key developmental time points from stem cell differentiation into mature organoid tissue. For data processing, we utilize the MitoGraph and MitoTNT software packages along with our developed custom computational tools. These tools allow for automated 4D organoid to single cell image processing and quantitative 4D single cell mitochondrial temporal network tracking. This work represents the first 4D high spatiotemporal-resolution quantification of live human organoid tissues at the single-cell level through development. We identified distinct mitochondrial phenotypes unique to each organoid type and found correlations between mitochondrial phenotypes, cellular age, and cell type. Furthermore, we demonstrate that mitochondrial network characteristics can predict both organoid type and cell age. Our findings reveal fundamental aspects of mitochondrial biology that were previously unobservable, offering new insights into cell-type-specific mitochondrial dynamics and enabling new findings in relevant human model systems. We believe that our findings and methods will be essential for advancing 4D cell biology, providing a powerful framework for characterizing organelles in organoid tissues.

cell biology↗

bioRxiv2024

LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), rely on a tilting of the sample plane with respect to the light sheet of 30-45 degrees to ease sample preparation. Data from such tilted-sample-plane LSFMs require subsequent deskewing and rotation for proper visualization and analysis. Such transformations currently demand substantial memory allocation. This poses computational challenges, especially with large datasets. The consequence is long processing times compared to data acquisition times, which currently limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a novel GPU-accelerated memory-efficient algorithm that integrates deskewing and rotation into a single transformation, significantly reducing memory requirements and reducing the preprocessing run time by at least 10-fold for large image stacks. Benchmarked against conventional methods and existing software, our approach demonstrates linear scalability. Processing large 3D stacks of up to 15 GB is now possible within one minute using a single GPU with 24 GB of memory. Applied to 4D LLSM datasets of human hepatocytes, human lung organoid tissue, and human brain organoid tissue, our method outperforms alternatives, providing rapid, accurate preprocessing within seconds. Importantly, such processing speeds now allow visualization of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data processing, visualization, and analysis on standard workstations, thereby revolutionizing biological imaging applications for LLSM, SPIM and similar light microscopes.

cell biology↗

bioRxiv2024

Phospho-KNL-1 recognition by a TPR domain targets the BUB-1/BUB-3 complex to C. elegans kinetochores

During mitosis, the Bub1-Bub3 complex concentrates at kinetochores, the microtubule-coupling interfaces on chromosomes, where it contributes to spindle checkpoint activation, kinetochore-spindle microtubule interactions, and protection of centromeric cohesion. Bub1 has a conserved N-terminal tetratricopeptide (TPR) domain followed by a binding motif for its conserved interactor Bub3. The current model for Bub1-Bub3 localization to kinetochores is that Bub3, along with its bound motif from Bub1, recognizes phosphorylated "MELT" motifs in the kinetochore scaffold protein Knl1. Motivated by the greater phenotypic severity of BUB-1 versus BUB-3 loss in C. elegans, we show that the BUB-1 TPR domain directly recognizes a distinct class of phosphorylated motifs in KNL-1 and that this interaction is essential for BUB-1-BUB-3 localization and function. BUB-3 recognition of phospho-MELT motifs additively contributes to drive super-stoichiometric accumulation of BUB-1-BUB-3 on its KNL-1 scaffold during mitotic entry. Bub1s TPR domain interacts with Knl1 in other species, suggesting that collaboration of TPR-dependent and Bub3-dependent interfaces in Bub1-Bub3 localization and functions may be conserved.

cell biology↗

bioRxiv2024

Far-red chemigenetic biosensors for multi-dimensional and super-resolved kinase activity imaging

Fluorescent biosensors revolutionized biomedical science by enabling the direct measurement of signaling activities in living cells, yet the current technology is limited in resolution and dimensionality. Here, we introduce highly sensitive chemigenetic kinase activity biosensors that combine the genetically encodable self-labeling protein tag HaloTag7 with bright far-red-emitting synthetic fluorophores. This technology enables five-color biosensor multiplexing, 4D activity imaging, and functional super-resolution imaging via stimulated emission depletion (STED) microscopy.

bioengineering↗

bioRxiv2023

Self-supervised deep learning uncovers the semantic landscape of drug-induced latent mitochondrial phenotypes

Imaging-based high-content screening aims to identify substances that modulate cellular phenotypes. Traditional approaches screen compounds for their ability to shift disease phenotypes toward healthy phenotypes, but these end point-based screens lack an atlas-like mapping between phenotype and cell state that covers the full spectrum of possible phenotypic responses. In this study, we present MitoSpace: a novel mitochondrial phenotypic atlas that leverages self-supervised deep learning to create a semantically meaningful latent space from images without relying on any data labels for training. Our approach employs a dataset of [~]100,000 microscopy images of Cal27 and HeLa cells treated with 25 drugs affecting mitochondria, but can be generalized to any cell type, cell organelle, or drug library with no changes to the methodology. We demonstrate how MitoSpace enhances our understanding of the range of mitochondrial phenotypes induced by pharmacological interventions. We find that i) self-supervised learning can automatically uncover the semantic landscape of drug induced latent mitochondrial phenotypes and can map individual cells to the correct functional area of the drug they are treated with, ii) the traditional classification of mitochondrial morphology along a fragmented to fused axis is more complex than previously thought, with additional axes being identified, and iii) latent spaces trained in a self-supervised manner are superior to those trained with supervised models, and generalize to other cell types and drug conditions without explicit training on those cell types or drug conditions. Future applications of MitoSpace include creating mitochondrial biomarkers for drug discovery and determining the effects of unknown drugs and diseases for diagnostic purposes.

pharmacology and toxicology↗

bioRxiv2022

Rapid iPSC inclusionopathy models shed light on formation, consequence and molecular subtype of alpha-synuclein inclusions

Intracellular inclusions accompanying neurodegeneration are histopathologically and ultrastructurally heterogeneous but the significance of this heterogeneity is unclear. iPSC models, while promising for disease modeling, do not form inclusions in a reasonable timeframe and suffer from limited tractability. Here, we developed an iPSC toolbox utilizing piggyBac-based or targeted transgenes to rapidly induce CNS cells with concomitant expression of aggregation-prone proteins. This system is amenable to screening and longitudinal tracking at single-cell and single-inclusion resolution. For proof-of-principle, cortical neuron -synuclein "inclusionopathy" models were engineered to form inclusions through exogenous seeding or -synuclein mutation. These models recapitulated known fibril- and lipid-rich inclusion subtypes, uncovering dynamic interactions between them, and refined the classification of inclusions in postmortem brain. Genetic-modifier and protein-interaction screens pinpointed proteins like RhoA whose sequestration into specific inclusion subtypes is likely to be toxic. This iPSC platform should enhance our understanding of proteinaceous pathologies in neurodegeneration and facilitate therapeutics development.

neuroscience↗

bioRxiv2022

MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data

Mitochondria form a network in the cell that rapidly changes through fission, fusion, and motility. This four-dimensional (4D, x,y,z,time) temporal network has only recently been made accessible through advanced imaging methods such as lattice light-sheet microscopy. Quantitative analysis tools for the resulting datasets however have been lacking. Here we present MitoTNT, the first-in-class software for Mitochondrial Temporal Network Tracking in 4D live-cell fluorescence microscopy data. MitoTNT uses spatial proximity and network topology to compute an optimal tracking. Tracking is >90% accurate in dynamic spatial mitochondria simulations and are in agreement with published motility results in vitro. Using MitoTNT, we reveal correlated mitochondrial movement patterns, local fission and fusion fingerprints, asymmetric fission and fusion dynamics, cross-network transport patterns, and network-level responses to pharmacological manipulations. MitoTNT is implemented in python with a JupyterLab interface. The extendable and user-friendly design aims at making temporal network tracking accessible to the wider mitochondria community.

cell biology↗