The development of novel plant-based meat alternatives that closely mimic the anisotropic structure of animal meat offers a solution to mitigate the adverse effects of animal meat consumption. The currently most widely adopted production route is shear processing through high-moisture extrusion (HME). The complex structure formation mechanisms that determine the final fibrous texture of extrudates have yet to be fully understood. The main obstacle is the lack of multiscale studies investigating the principles governing structure formation from the nano- to the macro-structural level. This work aims to address this knowledge gap by studying materials, collected after a dead-stop operation of an industrial pilot-plant scale extruder, with multiple characterisation techniques, such as Magnetic Resonance Imaging (MRI) and Small-Angle Scattering (SAS). We demonstrate that the nm- to μm-scale structure is formed already within the extruder barrels, and that sub-mm-scale anisotropy develops within the cooling die. Furthermore, we show that diffuse light reflectance (DR) probes the size and coarseness of the lamellar phase-separated regions.

High-moisture extrusion (HME) is a proven industrial food processing technique used to create textured plant-protein materials that can serve as alternatives for animal meat. The required multiscale anisotropic structure of the extrudate can be achieved by selecting suitable HME process conditions, as well as by pH-shifting. In this work, we explored pH-shifting via the water feed, which is an attractive industrially-scalable approach. Soy protein concentrate (SPC) was extruded on lab-scale and extrudates were characterized ex situ, from molecular to mm scale, using Diffuse Reflectance (DR), Magnetic Resonance Imaging (MRI), Small-Angle-Scattering of Neutrons (SANS) or X-rays (SAXS). pH-shifting had a non-monotonic effect on extrudate hardness and anisotropic structure at both sub-mm (MRI) and μm (DR) scale. At the sub-μm scale, SANS and SAXS data indicated that, at pH > pI, the radius of protein nano-aggregates monotonically increases, accompanied by a transition from particulate to fibrillar protein aggregation. When pH was further shifted to alkaline conditions, the decrease in clustering strength and nematic order parameter pointed to an increase in intra- and inter-fibrillar repulsion, respectively. Protein extractability experiments indicated that the effects of pH-shifting on anisotropic structure formation could not be attributed to covalent intermolecular crosslinking. Thus, repulsive non-covalent electrostatic protein-protein interactions play a dominant role in the formation of multiscale anisotropic structure during SPC extrusion. The formation of an optimal anisotropic SPC extrudate structure is determined by the pH-dependent balance between fibrillar nano-aggregate clustering and electrostatic repulsion. Alkalization or acidification via the water feed implies that protein charge and structure may not be in equilibrium yet with the imposed pH conditions. The transient nature of pH-shifting via the water feed results in an intricate interplay with extrusion conditions. Therefore, control of anisotropic structure formation, via the water feed, in SPC extrudates, is extruder specific.

CRISPR-Cas systems have widely been adopted as genome editing tools, with two frequently employed Cas nucleases being SpyCas9 and LbCas12a. Although both nucleases use RNA guides to find and cleave target DNA sites, the two enzymes differ in terms of protospacer-adjacent motif (PAM) requirements, guide architecture and cleavage mechanism. In the last years, rational engineering led to the creation of PAM-relaxed variants SpRYCas9 and impLbCas12a to broaden the targetable DNA space. By employing their catalytically inactive variants (dCas9/dCas12a), we quantified how the protein-specific characteristics impact the target search process. To allow quantification, we fused these nucleases to the photoactivatable fluorescent protein PAmCherry2.1 and performed single-particle tracking in cells of Escherichia coli. From our tracking analysis, we derived kinetic parameters for each nuclease with a non-targeting RNA guide, strongly suggesting that interrogation of DNA by LbdCas12a variants proceeds faster than that of SpydCas9. In the presence of a targeting RNA guide, both simulations and imaging of cells confirmed that LbdCas12a variants are faster and more efficient in finding a specific target site. Our work demonstrates the trade-off of relaxing PAM requirements in SpydCas9 and LbdCas12a using a powerful framework, which can be applied to other nucleases to quantify their DNA target search.

Vink et al. tracked single CRISPR RNA-surveillance complexes (Cascade) in the native host cell and determined the influence of Cascade copy numbers, PAM scanning speed, and the presence of CRISPR arrays and transcription on their ability to find and clear invading mobile genetic elements from the cell.

Single-particle tracking is an important technique in the life sciences to understand the kinetics of biomolecules. The analysis of apparent diffusion coefficients in vivo, for example, enables researchers to determine whether biomolecules are moving alone, as part of a larger complex, or are bound to large cellular components such as the membrane or chromosomal DNA. A remaining challenge has been to retrieve quantitative kinetic models, especially for molecules that rapidly switch between different diffusional states. Here, we present analytical diffusion distribution analysis (anaDDA), a framework that allows for extracting transition rates from distributions of apparent diffusion coefficients calculated from short trajectories that feature less than 10 localizations per track. Under the assumption that the system is Markovian and diffusion is purely Brownian, we show that theoretically predicted distributions accurately match simulated distributions and that anaDDA outperforms existing methods to retrieve kinetics, especially in the fast regime of 0.1–10 transitions per imaging frame. AnaDDA does account for the effects of confinement and tracking window boundaries. Furthermore, we added the option to perform global fitting of data acquired at different frame times to allow complex models with multiple states to be fitted confidently. Previously, we have started to develop anaDDA to investigate the target search of CRISPR-Cas complexes. In this work, we have optimized the algorithms and reanalyzed experimental data of DNA polymerase I diffusing in live Escherichia coli. We found that long-lived DNA interaction by DNA polymerase are more abundant upon DNA damage, suggesting roles in DNA repair. We further revealed and quantified fast DNA probing interactions that last shorter than 10 ms. AnaDDA pushes the boundaries of the timescale of interactions that can be probed with single-particle tracking and is a mathematically rigorous framework that can be further expanded to extract detailed information about the behavior of biomolecules in living cells.

CRISPR-Cas9 is widely used in genomic editing, but the kinetics of target search and its relation to the cellular concentration of Cas9 have remained elusive. Effective target search requires constant screening of the protospacer adjacent motif (PAM) and a 30 ms upper limit for screening was recently found. To further quantify the rapid switching between DNA-bound and freely-diffusing states of dCas9, we developed an open-microscopy framework, the miCube, and introduce Monte-Carlo diffusion distribution analysis (MC-DDA). Our analysis reveals that dCas9 is screening PAMs 40% of the time in Gram-positive Lactoccous lactis, averaging 17 ± 4 ms per binding event. Using heterogeneous dCas9 expression, we determine the number of cellular target-containing plasmids and derive the copy number dependent Cas9 cleavage. Furthermore, we show that dCas9 is not irreversibly bound to target sites but can still interfere with plasmid replication. Taken together, our quantitative data facilitates further optimization of the CRISPR-Cas toolbox.