During liver fibrosis, recurrent hepatic injuries lead to the accumulation of collagen and other extracellular matrix components in the interstitial space, ultimately disrupting liver functions. Early stages of liver fibrosis may be reversible, but opportunities for diagnosis at these stages are currently limited. Here, we show that the alterations of the interstitial space associated with fibrosis can be probed by tracking individual fluorescent single-walled carbon nanotubes (SWCNTs) diffusing in that space. In a mouse model of early liver fibrosis, we find that nanotubes generally explore elongated areas, whose lengths decrease as the disease progresses, even in regions where histopathological examination does not reveal fibrosis yet. Furthermore, this decrease in nanotube mobility is a purely geometrical effect as the instantaneous nanotube diffusivity stays unmodified. This work establishes the promise of SWCNTs both for diagnosing liver fibrosis at an early stage and for more in-depth studies of the biophysical effects of the disease.

The extracellular space (ECS) and its constituents play a crucial role in brain development, plasticity, circadian rhythm, and behavior, as well as brain diseases. Yet, since this compartment has an intricate geometry and nanoscale dimensions, its detailed exploration in live tissue has remained an unmet challenge. Here, we used a combination of single-nanoparticle tracking and super-resolution microscopy approaches to map the nanoscale dimensions of the ECS across the rodent hippocampus. We report that these dimensions are heterogeneous between hippocampal areas. Notably, stratum radiatum CA1 and CA3 ECS differ in several characteristics, a difference that gets abolished after digestion of the extracellular matrix. The dynamics of extracellular immunoglobulins vary within these areas, consistent with their distinct ECS characteristics. Altogether, we demonstrate that ECS nanoscale anatomy and diffusion properties are widely heterogeneous across hippocampal areas, impacting the dynamics and distribution of extracellular molecules.

During replication, expression, and repair of the eukaryotic genome, cellular machinery must access the DNA wrapped around histone proteins forming nucleosomes. These octameric protein·DNA complexes are modular, dynamic, and flexible and unwrap or disassemble either spontaneously or by the action of molecular motors. Thus, the mechanism of formation and regulation of subnucleosomal intermediates has gained attention genome-wide because it controls DNA accessibility. Here, we imaged nucleosomes and their more compacted structure with the linker histone H1 (chromatosomes) using high-speed atomic force microscopy to visualize simultaneously the changes in the DNA and the histone core during their disassembly when deposited on mica. Furthermore, we trained a neural network and developed an automatic algorithm to track molecular structural changes in real time. Our results show that nucleosome disassembly is a sequential process involving asymmetrical stepwise dimer ejection events. The presence of H1 restricts DNA unwrapping, significantly increases the nucleosomal lifetime, and affects the pathway in which heterodimer asymmetrical dissociation occurs. We observe that tetrasomes are resilient to disassembly and that the tetramer core (H3·H4)2 can diffuse along the nucleosome positioning sequence. Tetrasome mobility might be critical to the proper assembly of nucleosomes and can be relevant during nucleosomal transcription, as tetrasomes survive RNA polymerase passage. These findings are relevant to understanding nucleosome intrinsic dynamics and their modification by DNA-processing enzymes.

Important applications of single-particle tracking (SPT) aim at deciphering the diffusion properties of single fluorescent nanoparticles immersed in heterogeneous environments, such as multi-cellular biological tissues. To maximize the particle localization precision in such complex environments, high numerical aperture objectives are often required, which intrinsically restrict depth-of-focus (DOF) to less than a micrometer and impedes recording long trajectories when particles escape the plane of focus. In this work, we show that a simple binary phase mask can work with the spherical aberration inevitably induced by thick sample inhomogeneities, to extend the DOF of a single-molecule fluorescence microscope over more than 4 μm. The effect of point-spread-function (PSF) engineering over spherical aberration regularizes inhomogeneities of the PSF along the optical axis by restricting it to a narrow distribution. This allows the use of a single fitting function (i.e. Gaussian function) to localize single emitters over the whole extended DOF. Application of this simple approach on diffusing nanoparticles demonstrate that SPT trajectories can be recorded on significantly longer times.

The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates on the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project.

Localization microscopy approaches with enhanced depth-of-field (EDoF) are commonly optimized using the Cramér-Rao bound (CRB) as a criterion. It is widely believed that the CRB can be attained in practice by using the maximum-likelihood estimator (MLE). This is, however, an approximation, of which we define in this paper the precise domain of validity. Exploring a wide range of settings and noise levels, we show that the MLE is efficient when the signal-to-noise ratio (SNR) is such that the localization standard deviation of a single molecule is less than 20 nm. Thus, our results provide an explicit and quantitative validity boundary for the use of the MLE in EDoF localization microscopy setups optimized with the CRB.

Measuring carbon nanotube diffusion is complex in 3D liquid environments. Single molecule fluorescence microscopy commonly provides nanotube trajectories in the 2D imaging plane with nanometer precisions but assessing the third dimension is more challenging task. To this aim, We will present two strategies based on point-spread function (PSF) engineering [1] or self-interfering PSF [2]. Because nanotubes are not spherical objects, 3D angular diffusion of the nanotubes shall also be considered. We will show that using a high-frame rate imaging (kHz) of nanotube movements, the autocorrelation time of nanotube fluorescence intensity can be computed in order to measure the rotational diffusion coefficient of the nanotubes. This further allows to estimate the length of the nanotubes either from the rotational diffusion coefficients alone, or by combining translational and rotational diffusion coefficients which has the advantage to avoid the requirement of knowing the solution viscosity or the SWCNT hydrodynamic diameter [3]. References [1] Gresil, Lee, et al. In preparation [2] Caceido, Lee, et al. In preparation [3] Lee & Cognet J. Appl. Phys. 128 (2020) 224301

Fluorescence microscopy has succeeded in attaining super-resolution localization of single emitters in cellular biology. However, 3D localization deep inside tissue is still challenging. A few years ago, we developed SELFI: self-interference 3D super-resolution microscopy, a framework for 3D single-molecule localization within multicellular specimens and tissues. Here, we extend the capability of SELFI to the near-infrared (NIR) region where carbon nanotubes (CNTs) are strong emitters. The aim of this work is to develop NIR SELFI for single-particle tracking applications of CNTs in live brain tissues or NIR quantum dots. SELFI uses a diffraction grating placed on the optical path of the sample image, generating an interference pattern within diffraction limited images of point emitters. A single image obtained with NIR SELFI contains two independent variables: the intensity distribution to extract the intensity centroid to determine the lateral localization, and the wavefront curvature (provided by the interfringes) to get the axial super-localization. SELFI was first developed to localize red emitting dyes and quantum dots. The performance of the system is examined by means of the standard deviation and root mean square error of the localizations. The experiments performed show that the 3D-precision and accuracy achieved with NIR SELFI are both below 100 nm for emission around 1000 nm and high photon budget. Therefore, we can now achieve 3D localization in the NIR, permitting 3D single-particle tracking of CNTs at video rate in complex environments.

We provide evidence of a local synaptic nanoenvironment in the brain extracellular space (ECS) lying within 500 nm of postsynaptic densities. To reveal this brain compartment, we developed a correlative imaging approach dedicated to thick brain tissue based on single-particle tracking of individual fluorescent single wall carbon nanotubes (SWCNTs) in living samples and on speckle-based HiLo microscopy of synaptic labels. We show that the extracellular space around synapses bears specific properties in terms of morphology at the nanoscale and inner diffusivity. We finally show that the ECS juxta-synaptic region changes its diffusion parameters in response to neuronal activity, indicating that this nanoenvironment might play a role in the regulation of brain activity.

Luminescent single wall carbon nanotubes are now well established, as unique nanoreporters to probe the brain extracellular space[1]. On the imaging side, this comes from their rich near-infrared optical properties, which also eventually be improved by sp3-chemical functionalization[2]. In addition, their uncommon 1D morphology is an important asset for tissue penetration, yet understanding their diffusion behavior in the complex brain extracellular network is challenging and necessitates dedicated analysis tools. To this aim, we have developed two novel approaches (i) based on the local analysis of trajectory contours to locally measure the nanoscale dimensions of the brain network [3] and (ii) based on the transient evaluation of anomalous carbon nanotube diffusion to delineate the ECS molecular diffusion landscape [4]. The application of these analytical tools to extract relevant biological parameters in physiological and pathological brain models will be presented [5]. References [1] Godin et al Nat. Nanotechnol. 12 (2017) 238-243 ; Gao, et al, Nanomaterials 7, 11, (2017) 393 ; Danné et al ACS Photonics, 5, 2, (2018) 359-364 [2] Mandal et al. Scientific Reports, 10 (2020) 5286 [3] Paviolo et al Methods 174 (2020) 91-99 [4] Lee et al, in preparation [5] Soria et al Nat. Commun., 11 (2020) 3440

The super-resolved tracking of single particles undergoing diffusion in a biological environment is a widely used technique that can yield valuable information, either about the diffusing particle itself [1], or about the environment in which the diffusion occurs [2–4]. As an example of the latter, single-walled carbon nanotubes have been used as bright and photostable near-infrared fluorophores to probe the geometry and viscosity of the brain extracellular space [2–4]. In general, diffusion in complex biological environments is anomalous and highly spatially heterogeneous. When extracting quantitative information from a single-particle trajectory, this heterogeneity must be quantified and taken into account. Specifically, we aim at estimating, at each point of the trajectory, the values of the local anomalous exponent and anomalous diffusion coefficient. In our case, these diffusion properties cannot be assumed to jump between a small, finite number of hidden states, contrary to most traditional single-particle tracking analyses [1]; rather, these properties are in fact varying quasi-continuously.

Phase masks used to extend the depth-of-field (DoF) of localization microscopes are often designed to increase localization accuracy despite a loss in detection probability. We propose a method to optimize DoF extended single-molecule localization techniques taking into account the trade-off between detection probability and localization accuracy.