Mahalanobis Distance for Class Averaging of Cryo-EM Images

Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a technique in which the 3D structure of a molecule needs to be determined from its contrast transfer function (CTF) affected, noisy 2D projection images taken at unknown viewing directions. One of the main challenges in cryo-EM is the typically low signal to noise ratio (SNR) of the acquired images. 2D classification of images, followed by class averaging, improves the SNR of the resulting averages, and is used for selecting particles from micrographs and for inspecting the particle images. We introduce a new affinity measure, akin to the Mahalanobis distance, to compare cryo-EM images belonging to different defocus groups. The new similarity measure is employed to detect similar images, thereby leading to an improved algorithm for class averaging. We evaluate the performance of the proposed class averaging procedure on synthetic datasets, obtaining state of the art classification.Comment: Final version accepted to the 14th IEEE International Symposium on Biomedical Imaging (ISBI 2017

Cytotoxicity in the Age of Nano: The Role of Fourth Period Transition Metal Oxide Nanoparticle Physicochemical Properties

A clear understanding of physicochemical factors governing nanoparticle toxicity is still in its infancy. We used a systematic approach to delineate physicochemical properties of nanoparticles that govern cytotoxicity. The cytotoxicity of fourth period metal oxide nanoparticles (NPs): TiO2, Cr2O3, Mn2O3, Fe2O3, NiO, CuO, and ZnO increases with the atomic number of the transition metal oxide. This trend was not cell-type specific, as observed in non-transformed human lung cells (BEAS-2B) and human bronchoalveolar carcinoma-derived cells (A549). Addition of NPs to the cell culture medium did not significantly alter pH. Physiochemical properties were assessed to discover the determinants of cytotoxicity: (1) point-of-zero charge (PZC) (i.e., isoelectric point) described the surface charge of NPs in cytosolic and lysosomal compartments; (2) relative number of available binding sites on the NP surface quantified by X-ray photoelectron spectroscopy was used to estimate the probability of biomolecular interactions on the particle surface; (3) band-gap energy measurements to predict electron abstraction from NPs which might lead to oxidative stress and subsequent cell death; and (4) ion dissolution. Our results indicate that cytotoxicity is a function of particle surface charge, the relative number of available surface binding sites, and metal ion dissolution from NPs. These findings provide a physicochemical basis for both risk assessment and the design of safer nanomaterials

Automatic particle detection in digitized electron micrographs

High resolution structural analysis of biological complexes can be carried out by single particle electron microscopy where a large number of particle images are available. Many approaches to automate the process of selection of particle positions from digitized electron micrograph images have been described, but so far none has proved as good as manual selection. This thesis describes a method which I have developed to locate such biological complexes by matching small boxed areas to a set of reference images using the radius of gyration, complemented by a series of other simple criteria. From the reference images, parameters such as the ratio between the average density of the central area and that in its surrounding band, and the density sum and variance are calculated. They are compared with corresponding values from a moving square window of densities extracted from the micrograph, and the coordinates of successfully matched candidate squares are recorded. Since the same particle is detected in a series of overlapping windows, candidates found to be within close proximity are grouped, and the best-fitting one is selected from each cluster. Along with a small stack of boxed reference images, a few specified parameter values, such as the particle radius and the minimum acceptable distance between particle centres are required to select the windows. Micrograph labels and other areas that do not contain appropriate specimens are automatically ignored in order to minimize false positives, and reduce the computing time. A computer program SLEUTH written to carry out this method of automatic particle detection includes a graphical user interface to assist the user in setting up the parameter values. The program has been tested successfully on a variety of different biological structures, from both negatively stained and ice-embedded specimens

Interpreting deep learning output for out-of-distribution detection

Commonly used AI networks are very self-confident in their predictions, even when the evidence for a certain decision is dubious. The investigation of a deep learning model output is pivotal for understanding its decision processes and assessing its capabilities and limitations. By analyzing the distributions of raw network output vectors, it can be observed that each class has its own decision boundary and, thus, the same raw output value has different support for different classes. Inspired by this fact, we have developed a new method for out-of-distribution detection. The method offers an explanatory step beyond simple thresholding of the softmax output towards understanding and interpretation of the model learning process and its output. Instead of assigning the class label of the highest logit to each new sample presented to the network, it takes the distributions over all classes into consideration. A probability score interpreter (PSI) is created based on the joint logit values in relation to their respective correct vs wrong class distributions. The PSI suggests whether the sample is likely to belong to a specific class, whether the network is unsure, or whether the sample is likely an outlier or unknown type for the network. The simple PSI has the benefit of being applicable on already trained networks. The distributions for correct vs wrong class for each output node are established by simply running the training examples through the trained network. We demonstrate our OOD detection method on a challenging transmission electron microscopy virus image dataset. We simulate a real-world application in which images of virus types unknown to a trained virus classifier, yet acquired with the same procedures and instruments, constitute the OOD samples

Engineered ferritin for lanthanide binding

Ferritin H-homopolymers have been extensively used as nanocarriers for diverse applications in the targeted delivery of drugs and imaging agents, due to their unique ability to bind the transferrin receptor (CD71), highly overexpressed in most tumor cells. In order to incorporate novel fluorescence imaging properties, we have fused a lanthanide binding tag (LBT) to the C-terminal end of mouse H-chain ferritin, HFt. The HFt-LBT possesses one high affinity Terbium binding site per each of the 24 subunits provided by six coordinating aminoacid side chains and a tryptophan residue in its close proximity and is thus endowed with strong FRET sensitization properties. Accordingly, the characteristic Terbium emission band at 544 nm for the HFt-LBT Tb(III) complex was detectable upon excitation of the tag enclosed at two order of magnitude higher intensity with respect to the wtHFt protein. X-ray data at 2.9 Å and cryo-EM at 7 Å resolution demonstrated that HFt-LBT is correctly assembled as a 24-mer both in crystal and in solution. On the basis of the intrinsic Tb(III) binding properties of the wt protein, 32 additional Tb(III) binding sites, located within the natural iron binding sites of the protein, were identified besides the 24 Tb(III) ions coordinated to the LBTs. HFt-LBT Tb(III) was demonstrated to be actively uptaken by selected tumor cell lines by confocal microscopy and FACS analysis of their FITC derivatives, although direct fluorescence from Terbium emission could not be singled out with conventional, 295–375 nm, fluorescence excitation