The endoplasmic reticulum (ER) is a highly interconnected membrane network that serves as a central site for protein synthesis and maturation. A crucial subset of ER-associated transcripts, termed secretome mRNAs, encode secretory, lumenal and integral membrane proteins, representing nearly one-third of human protein-coding genes. Unlike cytosolic mRNAs, secretome mRNAs undergo co-translational translocation, and thus require precise coordination between translation and protein insertion. Disruption of this process, such as through altered elongation rates, activates stress response pathways that impede cellular growth, raising the question of whether secretome translation is spatially organized to ensure fidelity. Here, using live-cell single-molecule imaging, we demonstrate that secretome mRNA translation is preferentially localized to ER junctions that are enriched with the structural protein lunapark and in close proximity to lysosomes. Lunapark depletion reduced ribosome density and translation efficiency of secretome mRNAs near lysosomes, an effect that was dependent on eIF2-mediated initiation and was reversed by the integrated stress response inhibitor ISRIB. Lysosome-associated translation was further modulated by nutrient status: amino acid deprivation enhanced lysosome-proximal translation, whereas lysosomal pH neutralization suppressed it. These findings identify a mechanism by which ER junctional proteins and lysosomal activity cooperatively pattern secretome mRNA translation, linking ER architecture and nutrient sensing to the production of secretory and membrane proteins.

bioRxiv preprint: https://doi.org/10.1101/2024.11.21.624573

Cancer cells adapt to nutrient stress by remodeling the repertoire of proteins on their surface, enabling survival and progression under starvation conditions. However, the molecular mechanisms by which nutrient cues reshape the cell surface proteome to influence cell behavior remain largely unresolved. Here, we show that acute glucose starvation, but not amino acid deprivation or mTOR inhibition, selectively impairs ER-to-Golgi export of specific cargoes, such as E-cadherin, in a SEC24C-dependent manner. Quantitative cell surface proteomics reveal that glucose deprivation remodels the cell surface proteome, notably reducing surface expression of key adhesion molecules. This nutrient-sensitive reprogramming enhances cell migration in vitro and promotes metastasis in vivo. Mechanistically, we show that AMPK and ULK1 signaling orchestrate this process independent of autophagy, with ULK1-mediated phosphorylation of SEC31A driving SEC24C-dependent COPII reorganization. These findings establish ER-to-Golgi trafficking as a nutrient-sensitive regulatory node that links metabolic stress to cell surface remodeling and metastatic potential.

Assigning valence—appeal or aversion—to gustatory stimuli and relaying it to higher-order brain regions to guide flexible behaviors is crucial to survival. Yet the neural circuit that transforms gustatory input into motivationally relevant signals remains poorly defined in any model system. In Drosophila melanogaster, substantial progress has been made in mapping the sensorimotor pathway for feeding and the architecture of the dopaminergic reinforcement system. However, where and how valence is first assigned to a taste has long been a mystery. Here, we identified a pair of subesophageal zone interneurons in Drosophila, termed Fox, that impart positive valence to sweet taste and convey this signal to the mushroom body, the fly’s associative learning center. We show that Fox neuron activity is necessary and sufficient to drive appetitive behaviors and can override a tastant’s intrinsic valence without impairing taste quality discrimination. Furthermore, Fox neurons transmit the positive valence to specific dopaminergic neurons that mediate appetitive memory formation. Our findings reveal a circuit mechanism that transforms sweet sensation into a reinforcing signal to support learned sugar responses. The Fox neurons exhibit a convergent–divergent “hourglass” circuit motif, acting as a bottleneck for valence assignment and distributing motivational signals to higher-order centers. This architecture confers both robustness and flexibility in reward processing—an organizational principle that may generalize across species.

Localisation microscopy often relies on detailed models of point-spread functions. For applications such as deconvolution or PSF engineering, accurate models for light propagation in imaging systems with a high numerical aperture are required. Different models have been proposed based on 2D Fourier transforms or 1D Bessel integrals. The most precise ones combine a vectorial description of the electric field and accurate aberration models. However, it may be unclear which model to choose as there is no comprehensive comparison between the Fourier and Bessel approaches yet. Moreover, many existing libraries are written in Java (e.g., our previous PSF generator software) or MATLAB, which hinders their integration into deep learning algorithms. In this work, we start from the original Richards-Wolf integral and revisit both approaches in a systematic way. We present a unifying framework in which we prove the equivalence between the Fourier and Bessel strategies and detail a variety of correction factors applicable to both of them. Then, we provide a high-performance implementation of our theoretical framework in the form of an open-source library that is built on top of PyTorch, a popular library for deep learning. It enables us to benchmark the accuracy and computational speed of different models and allows for an in-depth comparison of the existing models for the first time. We show that the Bessel strategy is optimal for axisymmetric beams, while the Fourier approach can be applied to more general scenarios. Our work enables the efficient computation of a point-spread function on CPU or GPU, which can then be included in simulation and optimisation pipelines.

Primary cilia are microtubule-based sensory organelles that have been conserved throughout eukaryotic evolution. As discussed in this Review, a cilium is an elongated and highly specialized structure, and, together with its ability to selectively traffic and concentrate proteins, lipids and second messengers, it creates a signaling environment distinct from the cell body. Ciliary signaling pathways adopt a bow-tie network architecture, in which diverse inputs converge on shared effectors and second messengers before diverging to multiple outputs. Unlike other cellular bow-tie systems, cells exploit ciliary geometry, compartmentalization and infrastructure to enhance sensitivity at multiple scales, from individual molecular reactions to entire signaling pathways. In cilia, integration of the bow-tie network architecture with their specialized structure and unique environment confers robustness and evolvability, which enables cilia to acquire diverse signaling roles. However, this versatility comes with vulnerability - rare mutations that disrupt the features most essential for cilia robustness cause multisystem ciliopathies.

Genetically-encoded fluorescent biosensors have revolutionized our understanding of complex systems by permitting the in situ observation of chemical activities. However, only a comparatively small set of chemical activities can be monitored, largely due to the need to identify protein domains that undergo conformational and/or association changes in response to a stimulus. Here, we present a strategy that can convert ’simple’ affinity binders such as nanobodies into biosensors for their innate targets by introducing a peptide sequence that competes for the binding site. We demonstrate proof-of-concept implementations of this ’NanoBlock’ design, developing sensors based on the ALFA nanobody and on the PDZ domain of Erbin. We show that these sensors can reliably detect their targets in vitro, in mammalian cells, and as part of fluorescence-activated cell sorting (FACS) experiments. In doing so, our strategy offers a way to strongly expand the range of cellular processes that can be probed using fluorescent biosensors.

Enzyme-based self-labeling tags enable the covalent attachment of synthetic molecules to proteins inside living cells. A frontier of this field is designing cell-permeable multifunctional ligands that contain fluorophores in combination with affinity tags or pharmacological agents. This is challenging since attachment of additional chemical moieties onto fluorescent ligands can adversely affect membrane permeability. To address this problem, we examined the chemical properties of rhodamine-based self-labeling tag ligands through the lens of medicinal chemistry. We found that the lactone-zwitterion equilibrium constant () of rhodamines inversely correlates with their distribution coefficients (log), suggesting that ligands based on dyes exhibiting low and high log values, such as Si-rhodamines, would efficiently enter cells. We designed cell-permeable multifunctional HaloTag ligands with a biotin moiety to purify mitochondria or a JQ1 appendage to translocate BRD4 within the nucleus. We found that translocation of BRD4 to constitutive heterochromatin in cells leads to apparent increases in transcriptional activity. These fluorescent reagents enable affinity capture and translocation of intracellular proteins in living cells, and our general design concepts will facilitate the design of multifunctional chemical tools for biology.

Preprint: https://doi.org/10.1101/2022.07.02.498544 
Preprint: https://doi.org/10.32388/0xcyuc 

Solar flares, email exchanges, and many natural or social systems exhibit bursty dynamics, with periods of intense activity separated by long inactivity. These patterns often follow power- law distributions in inter-event intervals or event rates. Existing models typically capture only one of these features and rely on non-local memory, which complicates analysis and mechanistic interpretation. We introduce a novel self-reinforcing point process whose event rates are governed by local, Markovian nonlinear dynamics and post-event resets. The model generates power-law tails for both inter-event intervals and event rates over a broad range of exponents observed empirically across natural and human phenomena. Compared to non-local models such as Hawkes processes, our approach is mechanistically simpler, highly analytically tractable, and also easier to simulate. We provide methods for model fitting and validation, establishing this framework as a versatile foundation for the study of bursty phenomena.

Signaling pathways induce stereotyped transcriptional changes as stem cells progress into mature cell types during embryogenesis. Signaling perturbations are necessary to discover which genes are responsive or insensitive to pathway activity. However, gene regulation is additionally dependent on cell state-specific factors like chromatin modifications or transcription factor binding. Thus, transcriptional profiles need to be assayed in single cells to identify potentially multiple, distinct perturbation responses among heterogeneous cell states in an embryo. In perturbation studies, comparing heterogeneous transcriptional states among experimental conditions often requires samples to be collected over multiple independent experiments, which can introduce confounding batch effects. We present Design-Aware Integration of Single Cell ExpEriments (DAISEE), a new algorithm that models perturbation responses in single-cell datasets collected according to complex experimental designs. We demonstrate that DAISEE improves upon a previously available integrative nonnegative matrix factorization framework, more efficiently separating perturbation responses from confounding variation. We use DAISEE to integrate newly collected single-cell RNA sequencing datasets from 5-h-old zebrafish embryos expressing optimized photoswitchable MEK (psMEK), which globally activates the extracellular signal-regulated kinase (ERK), a signaling molecule involved in many cell specification events. psMEK drives some cells that are normally not exposed to ERK signals toward other wild type states and induces novel states expressing early-acting endothelial genes. Overactive signaling is therefore capable of producing unexpected gene expression states in developing embryos.

bioRxiv preprint: https://www.doi.org/10.1101/2024.09.05.610903

Optical nanoscopy of intact biological specimens has been transformed by recent advancements in hydrogel-based tissue clearing and expansion, enabling the imaging of cellular and subcellular structures with molecular contrast. However, existing high-resolution fluorescence microscopes are physically limited by objective-to-specimen distance, which prevents the study of whole-mount specimens without physical sectioning. To address this challenge, we developed a photochemical strategy for spatially precise sectioning of specimens. By combining serial photochemical sectioning with lattice light-sheet imaging and petabyte-scale computation, we imaged and reconstructed axons and myelin sheaths across entire mouse olfactory bulbs at nanoscale resolution. An olfactory bulb–wide analysis of myelinated and unmyelinated axons revealed distinctive patterns of axon degeneration and de-/dysmyelination in the neurodegenerative brain, highlighting the potential for peta- to exabyte-scale super-resolution studies using this approach. High-resolution microscopes have a short working distance, making it difficult to see deep within large biological samples such as an intact brain. Slicing the tissue with a blade can reach deeper, but this often distorts or destroys the fine structures that scientists want to study. By embedding a sample in a light-sensitive hydrogel, Wang et al. demonstrated a gentler approach using a precise ray or sheet of light to dissolve or cut away tissue layer by layer. After each layer is removed, the newly exposed surface is imaged, allowing for a complete, high-resolution, three-dimensional reconstruction without damaging physical contact. 

bioRxiv preprint: https://www.biorxiv.org/content/10.1101/2024.08.01.605857v1