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We present an oblique plane microscope (OPM) that uses a bespoke glass-tipped tertiary objective to improve the resolution, field of view, and usability over previous variants. Owing to its high numerical aperture optics, this microscope achieves lateral and axial resolutions that are comparable to the square illumination mode of lattice light-sheet microscopy, but in a user friendly and versatile format. Given this performance, we demonstrate high-resolution imaging of clathrin-mediated endocytosis, vimentin, the endoplasmic reticulum, membrane dynamics, and Natural Killer-mediated cytotoxicity. Furthermore, we image biological phenomena that would be otherwise challenging or impossible to perform in a traditional light-sheet microscope geometry, including cell migration through confined spaces within a microfluidic device, subcellular photoactivation of Rac1, diffusion of cytoplasmic rheological tracers at a volumetric rate of 14 Hz, and large field of view imaging of neurons, developing embryos, and centimeter-scale tissue sections.

Optical tissue clearing has revolutionized researchers’ ability to perform fluorescent measurements of molecules, cells, and structures within intact tissue. One common complication to all optically cleared tissue is a spatially heterogeneous refractive index, leading to light scattering and first-order defocus. We designed C-DSLM (cleared tissue digital scanned light-sheet microscopy) as a low-cost method intended to automatically generate in-focus images of cleared tissue. We demonstrate the flexibility and power of C-DSLM by quantifying fluorescent features in tissue from multiple animal models using refractive index matched and mismatched microscope objectives. This includes a unique measurement of myelin tracks within intact tissue using an endogenous fluorescent reporter where typical clearing approaches render such structures difficult to image. For all measurements, we provide independent verification using standard serial tissue sectioning and quantification methods. Paired with advancements in volumetric image processing, C-DSLM provides a robust methodology to quantify sub-micron features within large tissue sections. Optical clearing of tissue has enabled optical imaging deeper into tissue due to significantly reduced light scattering. Here, Ryan et al . tackle first-order defocus, an artefact of a non-uniform refractive index, extending light-sheet microscopy to partially cleared samples.

State space models, such as Mamba, have recently garnered attention in time series forecasting (TSF) due to their ability to capture sequence patterns. However, in electricity consumption benchmarks, Mamba forecasts exhibit a mean error of approximately 8%. Similarly, in traffic occupancy benchmarks, the mean error reaches 18%. This discrepancy leaves us to wonder whether the prediction is simply inaccurate or falls within error given spread in historical data. To address this limitation, we propose a method to quantify the predictive uncertainty of Mamba forecasts. To achieve this, we propose a dual-network framework based on the Mamba architecture for probabilistic forecasting, where one network generates point forecasts while the other estimates predictive uncertainty by modeling variance. We abbreviate our tool, Mamba with probabilistic TSF, as Mamba-ProbTSF and the code for its implementation is available on GitHub https://github.com/PessoaP/Mamba-ProbTSF . Evaluating this approach on synthetic and real-world benchmark datasets, we find Kullback–Leibler divergence between the learned distributions and the data–which, in the limit of infinite data, should converge to zero if the model correctly captures the underlying probability distribution–reduced to the order of 10 −3 for synthetic data and 10 −1 for real-world benchmark. We find that in both the electricity consumption and traffic occupancy benchmark, the true trajectory stays within the predicted uncertainty interval at the two-sigma level about 95% of the time. We further compare Mamba-ProbTSF against leading probabilistic forecast methods, DeepAR and ARIMA, and show that our method consistently achieves lower forecast errors while offering more reliable uncertainty quantification. We end with a consideration of potential limitations, adjustments to improve performance, and considerations for applying this framework to processes for purely or largely stochastic dynamics where the stochastic changes accumulate as observed, for example, in pure Brownian motion or molecular dynamics trajectories.

Tissue clearing has become a powerful technique for studying anatomy and morphology at scales ranging from entire organisms to subcellular features. With the recent proliferation of tissue-clearing methods and imaging options, it can be challenging to determine the best clearing protocol for a particular tissue and experimental question. The fact that so many clearing protocols exist suggests there is no one-size-fits-all approach to tissue clearing and imaging. Even in cases where a basic level of clearing has been achieved, there are many factors to consider, including signal retention, staining (labeling), uniformity of transparency, image acquisition and analysis. Despite reviews citing features of clearing protocols, it is often unknown a priori whether a protocol will work for a given experiment, and thus some optimization is required by the end user. In addition, the capabilities of available imaging setups often dictate how the sample needs to be prepared. After imaging, careful evaluation of volumetric image data is required for each combination of clearing protocol, tissue type, biological marker, imaging modality and biological question. Rather than providing a direct comparison of the many clearing methods and applications available, in this tutorial we address common pitfalls and provide guidelines for designing, optimizing and imaging in a successful tissue-clearing experiment with a focus on light-sheet fluorescence microscopy (LSFM). This tutorial provides guidance for selecting and optimizing tissue-clearing protocols for specific samples and biological questions. In addition, instructions are provided for developing an imaging strategy and processing the resulting data.

In biological imaging, there is a demand for cost-effective, high-resolution techniques to study dynamic intracellular processes. Structured illumination microscopy (SIM) is ideal for achieving high axial and lateral resolution in live samples due to its optical sectioning and low phototoxicity. However, conventional SIM systems remain expensive and complex. We introduce openSIMMO, an open-source, fully-automated SIM module compatible with commercial microscopes, supporting dual-color excitation. Our design uses affordable single-mode fiber-coupled lasers and a digital micromirror device (DMD), integrated with the open-source ImSwitch software for real-time super-resolution imaging. This setup offers up to 1.55-fold improvement in lateral resolution over wide-field microscopy. To optimize DMD diffraction, we developed a model for tilt and roll pixel configurations, enabling use with various low-cost projectors in SIM setups. Our goal is to democratize SIM-based super-resolution microscopy by providing open-source documentation and a flexible software framework adaptable to various hardware (e.g., cameras, stages) and reconstruction algorithms, enabling more widespread super-resolution upgrades across devices. Haoran Wang and co-authors present an open-source, automated two color structured illumination microscopy module compatible with standard microscopes. Combining low-cost components and real-time super-resolution imaging via open-source software, the system improves resolution by 1.55-fold while reducing complexity and cost.

Despite substantial experimental and computational efforts, mechanistic modeling remains more predictive in engineering than in systems biology. The reason for this discrepancy is not fully understood. One might argue that the randomness and complexity of biological systems are the main barriers to predictive understanding, but these issues are not unique to biology. Instead, we hypothesize that the specific shapes of rare single-molecule event distributions produce substantial yet overlooked challenges for biological models. We demonstrate why modern statistical tools to disentangle complexity and stochasticity, which assume normally distributed fluctuations or enormous datasets, do not apply to the discrete, positive, and nonsymmetric distributions that characterize mRNA fluctuations in single cells. As an example, we integrate single-molecule measurements and advanced computational analyses to explore mitogen-activated protein kinase induction of multiple stress response genes. Through systematic analyses of different metrics to compare the same model to the same data, we elucidate why standard modeling approaches yield nonpredictive models for single-cell gene regulation. We further explain how advanced tools recover precise, reproducible, and predictive understanding of transcription regulation mechanisms, including gene activation, polymerase initiation, elongation, mRNA accumulation, spatial transport, and decay.

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction methods, commonly implemented in the Fourier domain, often do not accurately model this noise. Such methods therefore suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised algorithms rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), a Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

Structured illumination microscopy (SIM) doubles the spatial resolution of a fluorescence microscope without requiring high laser powers or specialized fluorophores. However, the excitation of out-of-focus fluorescence can accelerate photobleaching and phototoxicity. In contrast, light-sheet fluorescence microscopy (LSFM) largely avoids exciting out-of-focus fluorescence, thereby enabling volumetric imaging with low photobleaching and intrinsic optical sectioning. Combining SIM with LSFM would enable gentle three-dimensional (3D) imaging at doubled resolution. However, multiple orientations of the illumination pattern, which are needed for isotropic resolution doubling in SIM, are challenging to implement in a light-sheet format. Here we show that multidirectional structured illumination can be implemented in oblique plane microscopy, an LSFM technique that uses a single objective for excitation and detection, in a straightforward manner. We demonstrate isotropic lateral resolution below 150 nm, combined with lower phototoxicity compared to traditional SIM systems and volumetric acquisition speed exceeding 1 Hz. The longstanding goal of combining the optical sectioning of light-sheet illumination and the resolving power of multidirectional structured illumination microscopy is realized using an oblique plane microscope for improved fast 3D subcellular imaging.

A common strategy to measure the efficacy of drug treatment is the comparison of ensemble readouts with and without treatment, such as proliferation and cell death. A fundamental assumption underlying this approach is that there exists minimal cell-to-cell variability in the response to a drug. Here, we demonstrate that ensemble and non-spatial single-cell readouts applied to primary cells may lead to incomplete conclusions due to cell-to-cell variability. We exposed primary fetal pulmonary artery endothelial cells (PAEC) isolated from healthy newborn sheep and persistent pulmonary hypertension of the newborn (PPHN) sheep to the growth hormone, insulin-like growth factor 1 (IGF-1). We found that IGF-1 increased proliferation and branch points in tube formation assays but not angiogenic signaling proteins at the population level for both cell types. We hypothesized that this molecular ambiguity was due to the presence of cellular sub-populations with variable responses to IGF-1. Using high throughput single-cell imaging, we discovered a spatially localized response to IGF-1. This suggests localized signaling or heritable cell response to external stimuli may ultimately be responsible for our observations. Discovering and further exploring these rare cells is critical to finding new molecular targets to restore cellular function.