Hydrogen properties and charges in a sub-1.2 Å resolution cryo-EM structure revealed by a cold field emission beam

The cold field emission (CFE) beam produces the less-attenuated contrast transfer function of electron microscopy, thereby enhancing high-resolution signals and this particularly benefits higher-resolution single particle cryogenic electron microscopy. Here, we present a sub-1.2 Å resolution structure of a standard protein sample, apoferritin. Image data were collected with the CFE beam in a high-throughput scheme while minimizing beam tilt deviations from the coma-free axis. A difference map reveals positive densities for most hydrogen atoms in the core region of the protein complex including those in water molecules, while negative densities around acidic amino-acid side chains likely represent negative charges. The position of the hydrogen densities depends on parent bonded-atom type, which is validated by an estimated level of coordinate errors.

Learning Single-Cell Perturbation Responses using Neural Optimal Transport

Understanding and predicting molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level. We apply our approach, CellOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CellOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.

Rational Design of a Porous Supramolecular Peptide Framework (SPF): A Crystalline Tetraproline in the Polyproline II Conformation

Herein, we present the first high resolution single crystal structure of an unfunctionalized tetrameric proline in the poly-proline II conformation. This rationally designed oligoproline tetramer, self-assembles to form a permanently porous crys-talline supramolecular peptide framework (SPF). Thermal activation, guest inclusion and thermally induced release of chemical guests have been demonstrated for this novel system. This discovery provides a conclusive insight into the pre-viously ambiguous conformation of short oligoprolines and will allow for the further development of proline-based pep-tide linkers in the rational design of SPFs and metal-peptide frameworks.

Identification of Small Molecules That Induce Therapeutic Levels of Fetal Hemoglobin for Treatment of Sickle Cell Disease By Pairing Machine Learning with High-Resolution Single Cell RNA Sequencing Maps of Adult and Fetal Human Erythropoiesis

Induction of fetal hemoglobin (HbF) to treat sickle cell disease (SCD) and beta-thalassemia has been validated as a clinical strategy to ameliorate these diseases. However, despite advances in cell and gene therapy to treat SCD, current approaches still require myeloablation and bone marrow transplantation, which carries risk and limits treatment accessibility. Currently, hydroxy urea (HU) is the only FDA approved small molecule for SCD that acts as a moderate inducer of HbF. Thus, the ability to develop a next generation small molecule with comparable efficacy to gene therapy approaches, but without the existing limitations of HU, would have immense clinical benefit. Towards the goal of identifying novel molecules that can robustly induce HbF, we generated a high-resolution single cell RNA sequencing (scRNAseq) dataset capturing pseudo-trajectories of adult and fetal erythropoiesis. This dataset allowed us to identify cell-states, cell-features, and gene-networks associated with fetal erythropoiesis in a data driven manner. In addition, by applying proprietary algorithms to this dataset, we identified a transcriptional signature targetable with a selected number of small molecules. Evaluation of these predicted small molecules in a 14-day human in vitro erythroid differentiation assay, identified a subset that induced HbF in mobilized peripheral blood (mPB) CD34+ hematopoietic stem and progenitor cells (HSPC) from healthy donors, as measured by % F-cells (flow cytometry) and % HbF (HPLC). CLT-1081 induced HbF (42.3% +17.26, n=4) above HU (17.16% +4.78, n=5) and BCL11A CRISPR knockdown (32.58% +10.66, n=5). We then measured globin gene expression to further characterize how CLT-1081 compared to HU induced changes in fetal and adult globin expression. CLT-1081 induced robust mRNA expression of both HBG1 and HBG2 and concomitant decrease in HBB transcript relative to HU. To determine if CLT-1081 acted by inducing the targeted transcriptional signature, we performed scRNAseq on a number of known HbF inducers, including CLT-1081, HU, and BCL11A CRISPR knockdown. Analysis revealed that CLT-1081 acted as predicted by our algorithms and activated the genes identified as drivers of HbF induction in the human in vitro erythroid differentiation assay. Furthermore, comparison between BCL11A CRISPR knockdown and CLT-1081 demonstrated strong correlation in terms of transcriptional response; CLT-1081 treatment reduced BCL11A expression in addition to other transcriptional changes, which elucidated a partial mechanism of action. In sum, we have used high dimensional data and computational tools in combination with established in vitro cell based assays to identify and validate a cell behavior marked by robust HbF expression and defined by a gene signature identified by our process. These findings demonstrate the power of Cellarity's platform, which augments a systems biology approach with machine learning to identify and develop drug candidates. Disclosures Cortes: Cellarity, Inc.: Current Employment, Current holder of stock options in a privately-held company. Monti: Cellarity, Inc.: Current Employment, Current holder of stock options in a privately-held company. Sun: Cellarity, Inc.: Current Employment, Current holder of stock options in a privately-held company. Lynch: Cellarity, Inc.: Current Employment, Current holder of stock options in a privately-held company. Xia: Cellarity, Inc.: Current Employment, Current holder of stock options in a privately-held company. Lin: Cellarity, Inc.: Current Employment, Current holder of stock options in a privately-held company. Malamas: Cellarity, Inc.: Current Employment, Current holder of stock options in a privately-held company. Steelman: Cellarity, Inc.: Current Employment, Current holder of stock options in a privately-held company. Krishnamoorthy: Cellarity, Inc.: Current Employment, Current holder of stock options in a privately-held company. Stewart: Cellarity, Inc.: Current Employment, Current holder of stock options in a privately-held company. Tozzo: Cellarity, Inc.: Current Employment, Current holder of stock options in a privately-held company.

Retrieval of Snow Albedo and Total Ozone Column from Single-View MSI/S-2 Spectral Reflectance Measurements over Antarctica

We proposed a simple algorithm to retrieve the total ozone column and snow properties (spectral albedo and effective light absorption path) using the high spatial resolution single–view MSI/S-2 measurements over Antarctica. In addition, the algorithm allows the retrieval of the snow grain size on a scale of 10–20 m. This algorithm should be useful for the understanding of intra-pixel total ozone and snow albedo variability in complement to satellite observations performed on a much coarser spatial resolution scale (0.3–1 km and even larger spatial scales).

Opportunities and Pitfalls of Fluorescent Labeling Methodologies for Extracellular Vesicle Profiling on High-Resolution Single-Particle Platforms

The relevance of extracellular vesicles (EVs) has grown exponentially, together with innovative basic research branches that feed medical and bioengineering applications. Such attraction has been fostered by the biological roles of EVs, as they carry biomolecules from any cell type to trigger systemic paracrine signaling or to dispose metabolism products. To fulfill their roles, EVs are transported through circulating biofluids, which can be exploited for the administration of therapeutic nanostructures or collected to intercept relevant EV-contained biomarkers. Despite their potential, EVs are ubiquitous and considerably heterogeneous. Therefore, it is fundamental to profile and identify subpopulations of interest. In this study, we optimized EV-labeling protocols on two different high-resolution single-particle platforms, the NanoFCM NanoAnalyzer (nFCM) and Particle Metrix ZetaView Fluorescence Nanoparticle Tracking Analyzer (F-NTA). In addition to the information obtained by particles’ scattered light, purified and non-purified EVs from different cell sources were fluorescently stained with combinations of specific dyes and antibodies to facilitate their identification and characterization. Despite the validity and compatibility of EV-labeling strategies, they should be optimized for each platform. Since EVs can be easily confounded with similar-sized nanoparticles, it is imperative to control instrument settings and the specificity of staining protocols in order to conduct a rigorous and informative analysis.