Biology subjects

Klein, A.

Publications and source records attributed to Klein, A..

11 recordsLinked to original sources

bioRxiv2018

Evaluating fMRI-Based Estimation of Eye Movement during Naturalistic Viewing

The collection of eye gaze information during functional magnetic resonance imaging (fMRI) is important for monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). However, the complexity and setup requirements of current in-scanner eye-tracking solutions can preclude many researchers from accessing such information. Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eyes orbit using a 1.5-minute calibration scan. Here, we provide confirmatory validation of the PEER methods ability to infer eye gaze on a TR-by-TR basis during movie viewing, using simultaneously acquired eye tracking data in five individuals (median angular deviation < 2{degrees}). Then, we examine variations in the predictive validity of PEER models across individuals in a subset of data (n=448) from the Child Mind Institute Healthy Brain Network Biobank, identifying head motion as a primary determinant. Finally, we accurately classify which of two movies is being watched based on the predicted eye gaze patterns (area under the curve = .90 {+/-} .02) and map the neural correlates of eye movements derived from PEER. PEER is a freely available and easy-to-use tool for determining eye fixations during naturalistic viewing.

neuroscience↗

bioRxiv2018

Passive Audio Vocal Capture and Measurement in the Evaluation of Selective Mutism

Selective Mutism (SM) is an anxiety disorder often diagnosed in early childhood and characterized by persistent failure to speak in certain social situations but not others. Diagnosing SM and monitoring treatment response can be quite complex, due in part to changing definitions of and scarcity of research about the disorder. Subjective self-reports and parent/teacher interviews can complicate SM diagnosis and therapy, given that similar speech problems of etiologically heterogeneous origin can be attributed to SM. The present perspective discusses the potential for passive audio capture to help overcome psychiatrys current lack of objective and quantifiable assessments in the context of SM. We present evidence from two pilot studies indicating the feasibility of using a digital wearable device to quantify child vocalization features affected by SM. We also highlight limitations in the design and implementation of this preliminary work that can help guide future efforts.

neuroscience↗

bioRxiv2017

Cytosolic delivery of large supramolecular proteincomplexes arranged on DNA nanopegboards

A generic methodology for cytosolic delivery of large supramolecular multiprotein complexes into living cells is described that takes advantage of the highly-controllable bottom-up fabrication of protein-decorated DNA nanostructures and the microfluidic \"cell squeezing\" technique. Therein, cells are deformed upon passage through a narrow constriction leading to formation of transient holes in the cell membrane that enable the diffusion of the protein-DNA nanostructures from the surrounding buffer into the cytosol. A diverse set of multiprotein complexes was assembled on DNA origami nanostructures using streptavidin and the sensitive glucose sensor protein FLIP as model systems. We demonstrate that our approach allows for the direct cytosolic delivery of these multifunctional protein complexes into the cytosol of HeLa cells. We also demonstrate that targeting groups can be incorporated into the protein-DNA nanoassemblies to enable their intracellular targeting to cytosolic compartments, such as the cytoskeleton or nucleus. We believe that this methodology will open up novel strategies for research in fundamental cell biology, such as the reverseengineering of the supramolecular machinery involved in gene regulation, cell signalling, or cell division. Furthermore, direct applications in immunotherapy can be foreseen.

cell biology↗

bioRxiv2017

Assessment of the impact of shared data on the scientific literature

Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice (e.g., workforce and infrastructural demands, sociocultural and privacy concerns, lack of standardization). To justify the significant effort required for sharing data (e.g., organization, curation, distribution), funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a brain imaging case study that provides direct evidence of the impact of open sharing on data use and resulting publications over a seven-year period (2010-2017). We dispel the myth that scientific findings using shared data cannot be published in high-impact journals and demonstrate rapid growth in the publication of such journal articles, scholarly theses, and conference proceedings. In contrast to commonly used pay to play models, we demonstrate that openly shared data can increase the scale (i.e., sample size) of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings suggest the transformative power of data sharing for accelerating science and underscore the need for the scientific ecosystem to embrace the challenge of implementing data sharing universally.

bioinformatics↗

bioRxiv2017

Assessing actimeters for inclusion in the Healthy Brain Network

BackgroundThe Healthy Brain Network is an openly shared pediatric psychiatric biobank with a target of 10,000 participants between the ages of 5 and 21, inclusively In adding ecological actimetry to the Healthy Brain Network, we intend to use appropriate, accurate, reliable tools. Currently a wide range of personal activity trackers are commercially available, providing a wide variety of sensor configurations. For many of these devices, accelerometry provides the basis of measuring both physical activity and sleep with comparable derivative measures.\n\nResultsIn order to include an ecological biotracker in the Healthy Brain Network protocol, we first evaluated the specifications of a variety of actimeters available for purchase. We then acquired physical instances of 5 of these devices (ActiGraph wGT3X-BT, Empatica Embrace, Empatica E4, GENEActiv Original, and Wavelet Wristband) and wore each of them in our daily lives, annotating our activities and evaluating the reasonableness of the data from each device and the logistical affordances of each device.\n\nConclusionsWe decided that the ActiGraph wGT3X-BT is the most appropriate device for inclusion in the Healthy Brain Network. However, none of the devices we evaluated was clearly superior or inferior to the rest; rather, each device seems to have use cases in which that device excels beyond the others.

bioinformatics↗

bioRxiv2017

Multiplexed Live-Cell Visualization Of Endogenous Proteins With Nanometer Precision By Fluorobodies

The visualization of endogenous proteins in living cells is a major challenge. A fundamental requirement for spatiotemporally precise imaging is a minimal disturbance of protein function at high signal-to-background ratio. Current approaches for visualization of native proteins in living cells are limited by dark emitting, bulky fluorescent proteins and uncontrollable expression levels. Here, we demonstrate the labeling of endogenous proteins using nanobodies with site-specifically engineered bright organic fluorophores, named fluorobodies. Their fast and fine-tuned intracellular transfer by microfluidic cell squeezing allowed for low background, low toxicity, and high-throughput. Multiplexed imaging of distinct cellular structures was facilitated by specific protein targeting, culminating in live-cell super-resolution imaging of protein networks. The high-throughput delivery of engineered nanobodies will open new avenues in visualizing native cellular structures with unprecedented accuracy in cell-based screens.

cell biology↗

bioRxiv2017

Mouse embryonic stem cells can differentiate via multiple paths to the same state

In embryonic development, cells must differentiate through stereotypical sequences of intermediate states to generate mature states of a particular fate. By contrast, direct programming can generate similar fates through alternative routes, by directly expressing terminal transcription factors. Yet the cell state transitions defining these new routes are unclear. We applied single-cell RNA sequencing to compare two mouse motor neuron differentiation protocols: a standard protocol approximating the embryonic lineage, and a direct programming method. Both undergo similar early neural commitment. Then, rather than transitioning through spinal intermediates like the standard protocol, the direct programming path diverges into a novel transitional state. This state has specific and abnormal gene expression. It opens a loop or worm hole in gene expression that converges separately onto the final motor neuron state of the standard path. Despite their different developmental histories, motor neurons from both protocols structurally, functionally, and transcriptionally resemble motor neurons from embryos.

developmental biology↗

bioRxiv2017

Clinically Useful Brain Imaging for Neuropsychiatry: How Can We Get There?

Despite decades of research, visions of transforming neuropsychiatry through the development of brain imaging-based growth charts or lab tests have remained out of reach. In recent years, there is renewed enthusiasm about the prospect of achieving clinically useful tools capable of aiding the diagnosis and management of neuropsychiatric disorders. The present work explores the basis for this enthusiasm. We assert that there is no single advance that currently has the potential to drive the field of clinical brain imaging forward. Instead, there has been a constellation of advances that, if combined, could lead to the identification of objective brain imaging-based markers of illness. In particular, we focus on advances that are helping to: 1) elucidate the research agenda for biological psychiatry (e.g., neuroscience focus, precision medicine), 2) shift research models for clinical brain imaging (e.g., big data exploration, standardization), 3) break down research silos (e.g., open science, calls for reproducibility and transparency), and 4) improve imaging technologies and methods. While an arduous road remains ahead, these advances are repositioning the brain imaging community for long-term success.

neuroscience↗

bioRxiv2017

Laminin α1 orchestrates VEGFA functions in the ecosystem of colorectal carcinoma

Tumor stroma remodeling is a key feature of malignant tumors and can promote cancer progression. Laminins are major constituents of basement membranes that physically separate the epithelium from the underlying stroma. By employing mouse models expressing high and low levels of the laminin 1 chain (LM1), we highlighted its implication in a tumorstroma crosstalk, thus leading to increased colon tumor incidence, angiogenesis and tumor growth. The underlying mechanism involves attraction of carcinoma-associated fibroblasts by LM1, VEGFA expression triggered by the complex integrin 2{beta}1-CXCR4 and binding of VEGFA to LM-111, which in turn promotes angiogenesis, tumor cell survival and proliferation. A gene signature comprising LAMA1, ITGB1, ITGA2, CXCR4 and VEGFA has negative predictive value in colon cancer. Together, this information opens novel opportunities for diagnosis and anti-cancer targeting.

cancer biology↗

bioRxiv2016

Mindboggling morphometry of human brains

Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains every conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, and more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggles algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.\n\nAuthor SummaryBrains vary in many ways, including their shape. Analysing differences in shape between brains or changes in brain shape over time has been used to characterize morphology of diseased brains, but these analyses conventionally rely on simple volumetric shape measures. We believe that access to a greater variety of shape measures could provide greater sensitivity and specificity to morphological disturbances, and could aid in diagnosis, tracking, and prediction of the progression of mental health disorders. Mindboggle is open source software that provides neuroscientists (and indeed, anyone interested in computing shapes) tools for computing a variety of shape measures, including area, volume, thickness, curvature, geodesic depth, travel depth, Laplace-Beltrami spectra, and Zernike moments. In addition to algorithmic contributions, we conducted evaluations and applied Mindboggle to conduct the most detailed shape analysis of human brains.

neuroscience↗

bioRxiv2016

SPRING: a kinetic interface for visualizing high dimensional single-cell expression data

MotivationSingle-cell gene expression profiling technologies can map the cell states in a tissue or organism. As these technologies become more common, there is a need for computational tools to explore the data they produce. In particular, existing data visualization approaches are imperfect for studying continuous gene expression topologies.\n\nResultsForce-directed layouts of k-nearest-neighbor graphs can visualize continuous gene expression topologies in a manner that preserves high-dimensional relationships and allows manually exploration of different stable two-dimensional representations of the same data. We implemented an interactive web-tool to visualize single-cell data using force-directed graph layouts, called SPRING. SPRING reveals more detailed biological relationships than existing approaches when applied to branching gene expression trajectories from hematopoietic progenitor cells. Visualizations from SPRING are also more reproducible than those of stochastic visualization methods such as tSNE, a state-of-the-art tool.\n\nAvailabilityhttps://kleintools.hms.harvard.edu/tools/spring.html,https://github.com/AllonKleinLab/SPRING/\n\nContactcalebsw@gmail.com, allon_klein@hms.harvard.edu

bioinformatics↗