Mitochondria-ER membrane contact sites (MERCS) represent a fundamental ultrastructural feature underlying unique biochemistry and physiology in eukaryotic cells. The ER protein PDZD8 is required for the formation of MERCS in many cell types, however, its tethering partner on the outer mitochondrial membrane (OMM) is currently unknown. Here we identify the OMM protein FKBP8 as the tethering partner of PDZD8 using a combination of unbiased proximity proteomics, CRISPR-Cas9 endogenous protein tagging, Cryo-electron tomography, and correlative light-electron microscopy. Single molecule tracking reveals highly dynamic diffusion properties of PDZD8 along the ER membrane with significant pauses and captures at MERCS. Overexpression of FKBP8 is sufficient to narrow the ER-OMM distance, whereas independent versus combined deletions of these two proteins demonstrate their interdependence for MERCS formation. Furthermore, PDZD8 enhances mitochondrial complexity in a FKBP8-dependent manner. Our results identify a novel ER-mitochondria tethering complex that regulates mitochondrial morphology in mammalian cells.

Preprint: 10.1101/2025.02.22.639343

Cell fate choice is a key event happening during preimplantation mouse development. From embryonic day 3.5 (E3.5) to E4.5, the inner cell mass (ICM) differentiates into epiblast (Epi, NANOG expressing cells) and primitive endoderm (PrE, GATA6, SOX17 and/or GATA4 expressing cells). The mechanism by which ICM cells differentiate into Epi cells and PrE cells remains partially unknown. FGF/ERK has been proposed as the main signalling pathway for this event, but it does not explain co-expression of NANOG and GAT6 or how the cell fate choice is initiated.

In this study, we investigate whether Wnt/β-catenin signalling also plays a role. To this end, we use two in vitro models based on inducible GATA6 expression: one in 2D, and another in 3D, namely ICM organoids. By combining these in vitro models with in vivo mouse embryos, chemical and classical genetics, and quantitative 3D immunofluorescence analyses, we propose a dual role for Wnt/β-catenin signalling.

We find that β-catenin, acting alongside FGF/ERK signalling, helps to guide the cell fate choice towards PrE. Additionally, by regulating GATA6 and GATA4 stability, β-catenin further facilitates this choice. To summarise, we observe that pathway activation promotes PrE differentiation, while its inhibition stalls it.

SUMMARY STATEMENT Wnt/β-catenin signalling promotes PrE fate in mouse preimplantation embryos.

Quantitative phase imaging (QPI) has proven to be a valuable tool for advanced biological and pharmacological research, providing phase information for the study of cell features and physiology in label-free conditions. The next step for QPI to become a gold standard is the quantitative assessment of the phase gradients over the different microscopy setups. Given the large variety of QPI systems, a systematic comparison is a challenging task, and requires a calibration target representative of the living samples. In this paper, we introduce a tailor-made 3D-printed phantom derived from phase images of eukaryotic cells. It comprises typical morphologies and optical thicknesses found in biological cultures and is characterized with digital holographic microscopy (reference measurements). The performance of three different full field QPI optical systems, in terms of optical path difference and dry mass accuracy, were evaluated. This phantom opens up other possibilities for the validation of reconstruction algorithms and post-processing routines, and paves the way for calibration targets designed ad hoc for specific biological questions.

SIGNIFICANCE: one-photon fluorescence imaging of calcium and voltage indicators expressed in neurons enables noninvasive recordings of neural activity with submillisecond precision. However, data acquisition speed is limited by the frame rate of cameras.

AIM: We developed a compressive streak fluorescence microscope to record fluorescence in individual neurons at high speeds ( frames per second) exceeding the nominal frame rate of the camera by trading off spatial pixels for temporal resolution.

APPROACH: Our microscope leverages a digital micromirror device for targeted illumination, a galvo mirror for temporal scanning, and a ridge regression algorithm for fast computational reconstruction of fluorescence traces with high temporal resolution.

RESULTS: In simulations, the ridge regression algorithm reconstructs traces of high temporal resolution with limited signal loss. Validation experiments with fluorescent beads and experiments in larval zebrafish demonstrate accurate reconstruction with a data compression ratio of 10 and accurate recordings of neural activity with 200- to 400-Hz sampling speeds.

CONCLUSIONS: Our compressive microscopy enables new experimental capabilities to monitor activity at a sampling speed that outpaces the nominal frame rate of the camera.

Genetically encoded calcium ion (Ca) indicators (GECIs) are widely-used molecular tools for functional imaging of Ca dynamics and neuronal activities with single-cell resolution. Here we report the design and development of two far-red fluorescent GECIs, FR-GECO1a and FR-GECO1c, based on the monomeric far-red fluorescent proteins mKelly1 and mKelly2. FR-GECOs have excitation and emission maxima at ~596 nm and ~644 nm, respectively, display large responses to Ca in vitro (ΔF/F = 6 for FR-GECO1a, 18 for FR-GECO1c), are bright under both one-photon and two-photon illumination, and have high affinities (apparent K = 29 nM for FR-GECO1a, 83 nM for FR-GECO1c) for Ca. FR-GECOs offer sensitive and fast detection of single action potentials in neurons, and enable in vivo all-optical manipulation and measurement of cellular activities in combination with optogenetic actuators.

Preprint: https://doi.org/10.1101/2020.11.12.380089

We address the problem of inferring the number of independently blinking fluorescent light emitters, when only their combined intensity contributions can be observed. This problem occurs regularly in light microscopy of objects smaller than the diffraction limit, where one wishes to count the number of fluorescently labeled subunits. Our proposed solution directly models the photophysics of the system, as well as the blinking kinetics of the fluorescent emitters as a fully differentiable hidden Markov model, estimating a posterior distribution of the total number of emitters. We show that our model is more accurate and increases the range of countable subunits by a factor of 2 compared to current state-of-the-art methods. Furthermore, we demonstrate that our model can be used to investigate the effect of blinking kinetics on counting ability and therefore can inform optimal experimental conditions.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques. It decomposes complex live-imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord.

Preprint: https://doi.org/10.1101/2024.05.02.592259

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. While wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement, and slow when serially mapping spatially varying aberrations across large fields of view. Here, we introduce AOViFT (Adaptive Optical Vision Fourier Transformer)---a machine learning-based aberration sensing framework built around a 3D multistage Vision Transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time, and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or post-acquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Hedonic eating is defined as food consumption driven by palatability without physiological need. However, neural control of palatable food intake is poorly understood. We discovered that hedonic eating is controlled by a neural pathway from the peri-locus ceruleus to the ventral tegmental area (VTA). Using photometry-calibrated optogenetics, we found that VTA dopamine (VTA) neurons encode palatability to bidirectionally regulate hedonic food consumption. VTA neuron responsiveness was suppressed during food consumption by semaglutide, a glucagon-like peptide receptor 1 (GLP-1R) agonist used as an antiobesity drug. Mice recovered palatable food appetite and VTA neuron activity during repeated semaglutide treatment, which was reversed by consumption-triggered VTA neuron inhibition. Thus, hedonic food intake activates VTA neurons, which sustain further consumption, a mechanism that opposes appetite reduction by semaglutide.

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, 'state cells'). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.