Extracting Transition Rates in Particle Tracking Using Analytical Diffusion Distribution Analysis

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.     

New and Old World phylogeography of pumpkinseed Lepomis gibbosus (Linnaeus, 1758): the North American origin of introduced populations in Europe

Hydrobiologia - To determine the origin of introduced European populations of pumpkinseed Lepomis gibbosus (Linnaeus, 1758)—a freshwater sunfish, native to eastern North America that has...     

Inhibition of melanoma development in the Nras(Q61K)::Ink4a−/− mouse model by the small molecule BI‐69A11

To date, there are no effective therapies for tumors bearing NRAS mutations, which are present in 15–20% of human melanomas. Here we extend our earlier studies where we demonstrated that the small molecule BI‐69A11 inhibits the growth of melanoma cell lines. Gene expression analysis revealed the induction of interferon‐ and cell death‐related genes that were associated with responsiveness of melanoma cell lines to BI‐69A11. Strikingly, the administration of BI‐69A11 inhibited melanoma development in genetically modified mice bearing an inducible form of activated Nras and a deletion of the Ink4a gene (Nras^(Q61K)::Ink4a^?/?). Biweekly administration of BI‐69A11 starting at 10 weeks or as late as 24 weeks after the induction of mutant Nras expression inhibited melanoma development (100 and 36%, respectively). BI‐69A11 treatment did not inhibit the development of histiocytic sarcomas, which constitute about 50% of the tumors in this model. BI‐69A11‐resistant Nras^(Q61K)::Ink4a^?/? tumors exhibited increased CD45 expression, reflective of immune cell infiltration and upregulation of gene networks associated with the cytoskeleton, DNA damage response, and small molecule transport. The ability to attenuate the development of NRAS mutant melanomas supports further development of BI‐69A11 for clinical assessment.     

Cortical Thickness,Surface Area and Subcortical Volume Differentially Contribute to Cognitive Heterogeneity in Parkinson’s Disease

Parkinson’s disease (PD) is often associated with cognitive deficits, although their severity varies considerably between patients. Recently, we used voxel-based morphometry (VBM) to show that individual differences in gray matter (GM) volume relate to cognitive heterogeneity in PD. VBM does, however, not differentiate between cortical thickness (CTh) and surface area (SA), which might be independently affected in PD. We therefore re-analyzed our cohort using the surface-based method FreeSurfer, and investigated (i) CTh, SA, and (sub)cortical GM volume differences between 93 PD patients and 45 matched controls, and (ii) the relation between these structural measures and cognitive performance on six neuropsychological tasks within the PD group. We found cortical thinning in PD patients in the left pericalcarine gyrus, extending to cuneus, precuneus and lingual areas and left inferior parietal cortex, bilateral rostral middle frontal cortex, and right cuneus, and increased cortical surface area in the left pars triangularis. Within the PD group, we found negative correlations between (i) CTh of occipital areas and performance on a verbal memory task, (ii) SA and volume of the frontal cortex and visuospatial memory performance, and, (iii) volume of the right thalamus and scores on two verbal fluency tasks. Our primary findings illustrate that i) CTh and SA are differentially affected in PD, and ii) VBM and FreeSurfer yield non-overlapping results in an identical dataset. We argue that this discrepancy is due to technical differences and the subtlety of the PD-related structural changes.     

Design‐to‐Device Approach Affords Panchromatic Co‐Sensitized Solar Cells

Data‐driven materials discovery has become increasingly important in identifying materials that exhibit specific, desirable properties from a vast chemical search space. Synergic prediction and experimental validation are needed to accelerate scientific advances related to critical societal applications. A design‐to‐device study that uses high‐throughput screens with algorithmic encodings of structure–property relationships is reported to identify new materials with panchromatic optical absorption, whose photovoltaic device applications are then experimentally verified. The data‐mining methods source 9431 dye candidates, which are auto‐generated from the literature using a custom text‐mining tool. These candidates are sifted via a data‐mining workflow that is tailored to identify optimal combinations of organic dyes that have complementary optical absorption properties such that they can harvest all available sunlight when acting as co‐sensitizers for dye‐sensitized solar cells (DSSCs). Six promising dye combinations are shortlisted for device testing, whereupon one dye combination yields co‐sensitized DSSCs with power conversion efficiencies comparable to those of the high‐performance, organometallic dye, N719. These results demonstrate how data‐driven molecular engineering can accelerate materials discovery for panchromatic photovoltaic or other applications.