Automated Identification of Subcellular Organelles by Coherent Anti-Stokes Raman Scattering

Coherent anti-Stokes Raman scattering (CARS) is an emerging tool for label-free characterization of living cells. Here, unsupervised multivariate analysis of CARS datasets was used to visualize the subcellular compartments. In addition, a supervised learning algorithm based on the “random forest” ensemble learning method as a classifier, was trained with CARS spectra using immunofluorescence images as a reference. The supervised classifier was then used, to our knowledge for the first time, to automatically identify lipid droplets, nucleus, nucleoli, and endoplasmic reticulum in datasets that are not used for training. These four subcellular components were simultaneously and label-free monitored instead of using several fluorescent labels. These results open new avenues for label-free time-resolved investigation of subcellular components in different cells, especially cancer cells.     

Development of a Membrane-Bound Random DNA Sequence Combinatorial Array Recognition Surface (CARS)

A partially overlapping population of random sequence 60mer DNA molecules consisting of many concatamers of varied lengths was spatially separated in one and two dimensions by electrophoresis in polyacrylamide and transferred to nitrocellulose membranes. The spatially separated library serves as a potential sensor interface on which many different molecular recognition events or target analyte-binding patterns may emerge, thereby theoretically representing a “universal sensor” surface. The separated DNA library has been referred to as a DNA combinatorial array recognition surface or “CARS.” After UV baking and various fluorescence staining or fluorescent probe interactions, the one-dimensional (1-D) and 2-D membrane-bound CARS were digitally photographed and subjected to image analysis with National Institutes of Health Image-Java software. Image analysis demonstrated relatively consistent and more similar spatial fluorescence patterns within CARS analyte treatment groups but noteworthy pattern differences before and after analyte addition and between different analyte treatments. Taken together, these data suggest a potential role for CARS as a novel, inexpensive, self-assembling universal molecular recognition surface that could be coupled to sophisticated Bayesian or other pattern recognition algorithms to classify analytes or make specific identifications, much like the senses of smell or taste.     

Spectral Fingerprinting of Individual Cells Visualized by Cavity-Reflection-Enhanced Light-Absorption Microscopy

The absorption spectrum of light is known to be a “molecular fingerprint” that enables analysis of the molecular type and its amount. It would be useful to measure the absorption spectrum in single cell in order to investigate the cellular status. However, cells are too thin for their absorption spectrum to be measured. In this study, we developed an optical-cavity-enhanced absorption spectroscopic microscopy method for two-dimensional absorption imaging. The light absorption is enhanced by an optical cavity system, which allows the detection of the absorption spectrum with samples having an optical path length as small as 10 μm, at a subcellular spatial resolution. Principal component analysis of various types of cultured mammalian cells indicates absorption-based cellular diversity. Interestingly, this diversity is observed among not only different species but also identical cell types. Furthermore, this microscopy technique allows us to observe frozen sections of tissue samples without any staining and is capable of label-free biopsy. Thus, our microscopy method opens the door for imaging the absorption spectra of biological samples and thereby detecting the individuality of cells.     

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features,Subcellular Locations and Interaction Networks

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein''s metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein''s metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.     

Immune fingerprinting through repertoire similarity

Immune repertoires provide a unique fingerprint reflecting the immune history of individuals, with potential applications in precision medicine. However, the question of how personal that information is and how it can be used to identify individuals has not been explored. Here, we show that individuals can be uniquely identified from repertoires of just a few thousands lymphocytes. We present “Immprint,” a classifier using an information-theoretic measure of repertoire similarity to distinguish pairs of repertoire samples coming from the same versus different individuals. Using published T-cell receptor repertoires and statistical modeling, we tested its ability to identify individuals with great accuracy, including identical twins, by computing false positive and false negative rates < 10⁻⁶ from samples composed of 10,000 T-cells. We verified through longitudinal datasets that the method is robust to acute infections and that the immune fingerprint is stable for at least three years. These results emphasize the private and personal nature of repertoire data.     

R package for animal behavior classification from accelerometer data—rabc

Increasingly, animal behavior studies are enhanced through the use of accelerometry. To allow translation of raw accelerometer data to animal behaviors requires the development of classifiers. Here, we present the “rabc” (r for animal behavior classification) package to assist researchers with the interactive development of such animal behavior classifiers in a supervised classification approach. The package uses datasets consisting of accelerometer data with their corresponding animal behaviors (e.g., for triaxial accelerometer data along the x, y and z axes arranged as “x, y, z, x, y, z,…, behavior”). Using an example dataset collected on white stork (Ciconia ciconia), we illustrate the workflow of this package, including accelerometer data visualization, feature calculation, feature selection, feature visualization, extreme gradient boost model training, validation, and, finally, a demonstration of the behavior classification results.     

Studies on Persistent Infections of Tissue Culture VI. Reversible Changes in Newcastle Disease Virus Populations as a Result of Passage in L Cells or Chick Embryos

Populations of the Victoria strain of Newcastle disease virus (NDV), reisolated from persistently infected L-cell cultures and passed twice in the embryonated hen's egg (NDV_L-E-2), were found to differ strikingly from the original, chick embryo-adapted virus (NDV_o). After exposure of L cells to NDV_o at high multiplicities of infection, all cells became abortively infected; they produced only small aggregates of viral antigen and few, if any, infectious virus particles, but they yielded large amounts of interferon. No cytopathic effects (CPE) were noted, and the cultures survived readily as viral carriers. In contrast, NDV_L-E-2 yielded under similar conditions large quantities of viral antigen and infectious virus particles, but no detectable interferon, and the cultures were rapidly destroyed. This change in “virulence” was at least partially reversible by further serial passages of NDV_L-E-2 in chick embryos, as was evident from a consecutive decrease in CPE with a concomitant increasingly rapid recovery of the L-cell cultures, gradually diminishing yields of infectious viral progeny, and the returning of a capacity to induce interferon synthesis. Thus, NDV_L-E-16 resembled NDV_o in many aspects, except for a less striking reduction in its ability to replicate in L cells. Although a selection of viral variants under the given sets of conditions has not been entirely excluded, the establishment of “avirulence” appears to be largely explained by a gradual accumulation of noninfectious, interferon-inducing components in the course of serial passages in the embryonated hen's egg, and the acquisition of “virulence” by a loss of these components. The evidence is as follows. (i) By a step-wise decrease in the dose of virus and restriction of the analyses to the first infectious cycle, a multiplicity of infection was ultimately reached for all “avirulent” populations at which infected cells produced normal yields of infectious viral progeny; i.e., the interferon-inducing components were diluted to noneffective levels. The lowest multiplicity which resulted in a measurable reduction in infectious virus replication was also the last one to induce detectable interferon synthesis. (ii) All viral clones derived from “avirulent” populations behaved like NDV_L-E-2 rather than like the parent viral suspensions, except that some of them elicited small amounts of interferon in L cells. The interferon-inducing components were reduced or lost in the cloning procedures. The nature of the interferon-inducing components has not been established. These components, which were neutralized by rabbit sera against “virulent” NDV_L-E-2 populations, may represent largely inactive or incomplete virus particles; however, the infectious virus-hemagglutinin ratios of “avirulent” populations were mostly of an order similar to those of “virulent” populations. The interferon-inducing components aborted the infectious process in cells simultaneously invaded by infectious virus particles. The implications of these findings are discussed.     

Logic Gate Operation by DNA Translocation through Biological Nanopores

Logical operations using biological molecules, such as DNA computing or programmable diagnosis using DNA, have recently received attention. Challenges remain with respect to the development of such systems, including label-free output detection and the rapidity of operation. Here, we propose integration of biological nanopores with DNA molecules for development of a logical operating system. We configured outputs “1” and “0” as single-stranded DNA (ssDNA) that is or is not translocated through a nanopore; unlabeled DNA was detected electrically. A negative-AND (NAND) operation was successfully conducted within approximately 10 min, which is rapid compared with previous studies using unlabeled DNA. In addition, this operation was executed in a four-droplet network. DNA molecules and associated information were transferred among droplets via biological nanopores. This system would facilitate linking of molecules and electronic interfaces. Thus, it could be applied to molecular robotics, genetic engineering, and even medical diagnosis and treatment.     

Mining Proteins with Non-Experimental Annotations Based on an Active Sample Selection Strategy for Predicting Protein Subcellular Localization

Subcellular localization of a protein is important to understand proteins’ functions and interactions. There are many techniques based on computational methods to predict protein subcellular locations, but it has been shown that many prediction tasks have a training data shortage problem. This paper introduces a new method to mine proteins with non-experimental annotations, which are labeled by non-experimental evidences of protein databases to overcome the training data shortage problem. A novel active sample selection strategy is designed, taking advantage of active learning technology, to actively find useful samples from the entire data pool of candidate proteins with non-experimental annotations. This approach can adequately estimate the “value” of each sample, automatically select the most valuable samples and add them into the original training set, to help to retrain the classifiers. Numerical experiments with for four popular multi-label classifiers on three benchmark datasets show that the proposed method can effectively select the valuable samples to supplement the original training set and significantly improve the performances of predicting classifiers.     

Imaging growth of neurites in conditioned hydrogel by coherent anti-Stokes Raman scattering microscopy

Cultured DRGs in different gel scaffolds were analyzed using CARS microscopy to determine its possible use as a label-free imaging option for tracking cellular growth in a gel scaffold. This study demonstrates for the first time the applicability of CARS microscopy to the imaging of live neuronal cells in GAG hydrogels. By tuning the laser beating frequency, ωp ? ωs, to match the vibration of C-H bonds in the cell membrane, the CARS signal yields detailed, high-quality images of neurites with single membrane detection sensitivity. The results demonstrate that CARS imaging allows monitoring of cellular growth in a tissue scaffold over time, with a contrast that shows comparable cellular structures to those obtained using standard fluorescent staining techniques. These findings show the potential of CARS microscopy to assist in the understanding of organogenesis processes in a tissue scaffold.     

Extracting Multiscale Pattern Information of fMRI Based Functional Brain Connectivity with Application on Classification of Autism Spectrum Disorders

We employed a multi-scale clustering methodology known as “data cloud geometry” to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI) protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs) in 29 individuals with autism spectrum disorders (ASD), and 29 individuals with typical development (TD) while they completed a cognitive control task. Connectivity clustering geometry was examined at both “fine” and “coarse” scales. At the coarse scale, the connectivity clustering geometry produced 10 valid clusters with a coherent relationship to neural anatomy. A supervised learning algorithm employed fine scale information about clustering motif configurations and prevalence, and coarse scale information about intra- and inter-regional connectivity; the algorithm correctly classified ASD and TD participants with sensitivity of and specificity of . Most of the predictive power of the logistic regression model resided at the level of the fine-scale clustering geometry, suggesting that cellular versus systems level disturbances are more prominent in individuals with ASD. This article provides validation for this multi-scale geometric approach to extracting brain functional connectivity pattern information and for its use in classification of ASD.     

Chemical contrast for imaging living systems: molecular vibrations drive CARS microscopy

Cellular biomolecules contain unique molecular vibrations that can be visualized by coherent anti-Stokes Raman scattering (CARS) microscopy without the need for labels. Here we review the application of CARS microscopy for label-free imaging of cells and tissues using the natural vibrational contrast that arises from biomolecules like lipids as well as for imaging of exogenously added probes or drugs. High-resolution CARS microscopy combined with multimodal imaging has allowed for dynamic monitoring of cellular processes such as lipid metabolism and storage, the movement of organelles, adipogenesis and host-pathogen interactions and can also be used to track molecules within cells and tissues. The CARS imaging modality provides a unique tool for biological chemists to elucidate the state of a cellular environment without perturbing it and to perceive the functional effects of added molecules.     

Analysis of Multiple Sarcoma Expression Datasets: Implications for Classification,Oncogenic Pathway Activation and Chemotherapy Resistance

Background

Diagnosis of soft tissue sarcomas (STS) is challenging. Many remain unclassified (not-otherwise-specified, NOS) or grouped in controversial categories such as malignant fibrous histiocytoma (MFH), with unclear therapeutic value. We analyzed several independent microarray datasets, to identify a predictor, use it to classify unclassifiable sarcomas, and assess oncogenic pathway activation and chemotherapy response.

Methodology/Principal Findings

We analyzed 5 independent datasets (325 tumor arrays). We developed and validated a predictor, which was used to reclassify MFH and NOS sarcomas. The molecular “match” between MFH and their predicted subtypes was assessed using genome-wide hierarchical clustering and Subclass-Mapping. Findings were validated in 15 paraffin samples profiled on the DASL platform. Bayesian models of oncogenic pathway activation and chemotherapy response were applied to individual STS samples. A 170-gene predictor was developed and independently validated (80-85% accuracy in all datasets). Most MFH and NOS tumors were reclassified as leiomyosarcomas, liposarcomas and fibrosarcomas. “Molecular match” between MFH and their predicted STS subtypes was confirmed both within and across datasets. This classification revealed previously unrecognized tissue differentiation lines (adipocyte, fibroblastic, smooth-muscle) and was reproduced in paraffin specimens. Different sarcoma subtypes demonstrated distinct oncogenic pathway activation patterns, and reclassified MFH tumors shared oncogenic pathway activation patterns with their predicted subtypes. These patterns were associated with predicted resistance to chemotherapeutic agents commonly used in sarcomas.

Conclusions/Significance

STS profiling can aid in diagnosis through a predictor tracking distinct tissue differentiation in unclassified tumors, and in therapeutic management via oncogenic pathway activation and chemotherapy response assessment.     

Network Signatures of Survival in Glioblastoma Multiforme

To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.     

A Familiar(ity) Problem: Assessing the Impact of Prerequisites and Content Familiarity on Student Learning

Prerequisites are embedded in most STEM curricula. However, the assumption that the content presented in these courses will improve learning in later courses has not been verified. Because a direct comparison of performance between students with and without required prerequisites is logistically difficult to arrange in a randomized fashion, we developed a novel familiarity scale, and used this to determine whether concepts introduced in a prerequisite course improved student learning in a later course (in two biology disciplines). Exam questions in the latter courses were classified into three categories, based on the degree to which the tested concept had been taught in the prerequisite course. If content familiarity mattered, it would be expected that exam scores on topics covered in the prerequisite would be higher than scores on novel topics. We found this to be partially true for “Very Familiar” questions (concepts covered in depth in the prerequisite). However, scores for concepts only briefly discussed in the prerequisite (“Familiar”) were indistinguishable from performance on topics that were “Not Familiar” (concepts only taught in the later course). These results imply that merely “covering” topics in a prerequisite course does not result in improved future performance, and that some topics may be able to removed from a course thereby freeing up class time. Our results may therefore support the implementation of student-centered teaching methods such as active learning, as the time-intensive nature of active learning has been cited as a barrier to its adoption. In addition, we propose that our familiarity system could be broadly utilized to aid in the assessment of the effectiveness of prerequisites.     

Reduction of Pavlovian Bias in Schizophrenia: Enhanced Effects in Clozapine-Administered Patients

The negative symptoms of schizophrenia (SZ) are associated with a pattern of reinforcement learning (RL) deficits likely related to degraded representations of reward values. However, the RL tasks used to date have required active responses to both reward and punishing stimuli. Pavlovian biases have been shown to affect performance on these tasks through invigoration of action to reward and inhibition of action to punishment, and may be partially responsible for the effects found in patients. Forty-five patients with schizophrenia and 30 demographically-matched controls completed a four-stimulus reinforcement learning task that crossed action (“Go” or “NoGo”) and the valence of the optimal outcome (reward or punishment-avoidance), such that all combinations of action and outcome valence were tested. Behaviour was modelled using a six-parameter RL model and EEG was simultaneously recorded. Patients demonstrated a reduction in Pavlovian performance bias that was evident in a reduced Go bias across the full group. In a subset of patients administered clozapine, the reduction in Pavlovian bias was enhanced. The reduction in Pavlovian bias in SZ patients was accompanied by feedback processing differences at the time of the P3a component. The reduced Pavlovian bias in patients is suggested to be due to reduced fidelity in the communication between striatal regions and frontal cortex. It may also partially account for previous findings of poorer “Go-learning” in schizophrenia where “Go” responses or Pavlovian consistent responses are required for optimal performance. An attenuated P3a component dynamic in patients is consistent with a view that deficits in operant learning are due to impairments in adaptively using feedback to update representations of stimulus value.