2,111 research outputs found
Probabilistic Fluorescence-Based Synapse Detection
Brain function results from communication between neurons connected by
complex synaptic networks. Synapses are themselves highly complex and diverse
signaling machines, containing protein products of hundreds of different genes,
some in hundreds of copies, arranged in precise lattice at each individual
synapse. Synapses are fundamental not only to synaptic network function but
also to network development, adaptation, and memory. In addition, abnormalities
of synapse numbers or molecular components are implicated in most mental and
neurological disorders. Despite their obvious importance, mammalian synapse
populations have so far resisted detailed quantitative study. In human brains
and most animal nervous systems, synapses are very small and very densely
packed: there are approximately 1 billion synapses per cubic millimeter of
human cortex. This volumetric density poses very substantial challenges to
proteometric analysis at the critical level of the individual synapse. The
present work describes new probabilistic image analysis methods for
single-synapse analysis of synapse populations in both animal and human brains.Comment: Current awaiting peer revie
STATISTICAL METHODS FOR THE ANALYSIS OF CANCER GENOME SEQUENCING DATA
The purpose of cancer genome sequencing studies is to determine the nature and types of alterations present in a typical cancer and to discover genes mutated at high frequencies. In this article we discuss statistical methods for the analysis of data generated in these studies. We place special emphasis on a two-stage study design introduced by Sjoblom et al.[1]. In this context, we describe statistical methods for constructing scores that can be used to prioritize candidate genes for further investigation and to assess the statistical signicance of the candidates thus identfied
Preserving Derivative Information while Transforming Neuronal Curves
The international neuroscience community is building the first comprehensive
atlases of brain cell types to understand how the brain functions from a higher
resolution, and more integrated perspective than ever before. In order to build
these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal
cortical neurons etc.) are traced in individual brain samples by placing points
along dendrites and axons. Then, the traces are mapped to common coordinate
systems by transforming the positions of their points, which neglects how the
transformation bends the line segments in between. In this work, we apply the
theory of jets to describe how to preserve derivatives of neuron traces up to
any order. We provide a framework to compute possible error introduced by
standard mapping methods, which involves the Jacobian of the mapping
transformation. We show how our first order method improves mapping accuracy in
both simulated and real neuron traces under random diffeomorphisms. Our method
is freely available in our open-source Python package brainlit
Probabilistic fluorescence-based synapse detection
Deeper exploration of the brain’s vast synaptic networks will require new tools for high-throughput structural and molecular profiling of the diverse populations of synapses that compose those networks. Fluorescence microscopy (FM) and electron microscopy (EM) offer complementary advantages and disadvantages for single-synapse analysis. FM combines exquisite molecular discrimination capacities with high speed and low cost, but rigorous discrimination between synaptic and non-synaptic fluorescence signals is challenging. In contrast, EM remains the gold standard for reliable identification of a synapse, but offers only limited molecular discrimination and is slow and costly. To develop and test single-synapse image analysis methods, we have used datasets from conjugate array tomography (cAT), which provides voxel-conjugate FM and EM (annotated) images of the same individual synapses. We report a novel unsupervised probabilistic method for detection of synapses from multiplex FM (muxFM) image data, and evaluate this method both by comparison to EM gold standard annotated data and by examining its capacity to reproduce known important features of cortical synapse distributions. The proposed probabilistic model-based synapse detector accepts molecular-morphological synapse models as user queries, and delivers a volumetric map of the probability that each voxel represents part of a synapse. Taking human annotation of cAT EM data as ground truth, we show that our algorithm detects synapses from muxFM data alone as successfully as human annotators seeing only the muxFM data, and accurately reproduces known architectural features of cortical synapse distributions. This approach opens the door to data-driven discovery of new synapse types and their density. We suggest that our probabilistic synapse detector will also be useful for analysis of standard confocal and super-resolution FM images, where EM cross-validation is not practical
Exact solution of a two-type branching process: Clone size distribution in cell division kinetics
We study a two-type branching process which provides excellent description of
experimental data on cell dynamics in skin tissue (Clayton et al., 2007). The
model involves only a single type of progenitor cell, and does not require
support from a self-renewed population of stem cells. The progenitor cells
divide and may differentiate into post-mitotic cells. We derive an exact
solution of this model in terms of generating functions for the total number of
cells, and for the number of cells of different types. We also deduce large
time asymptotic behaviors drawing on our exact results, and on an independent
diffusion approximation.Comment: 16 page
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Multi-Probe Investigation of Proteomic Structure of Pathogens
Complete genome sequences are available for understanding biotransformation, environmental resistance and pathogenesis of microbial, cellular and pathogen systems. The present technological and scientific challenges are to unravel the relationships between the organization and function of protein complexes at cell, microbial and pathogens surfaces, to understand how these complexes evolve during the bacterial, cellular and pathogen life cycles, and how they respond to environmental changes, chemical stimulants and therapeutics. In particular, elucidating the molecular structure and architecture of human pathogen surfaces is essential to understanding mechanisms of pathogenesis, immune response, physicochemical interactions, environmental resistance and development of countermeasures against bioterrorist agents. The objective of this project was to investigate the architecture, proteomic structure, and function of bacterial spores through a combination of high-resolution in vitro atomic force microscopy (AFM) and AFM-based immunolabeling with threat-specific antibodies. Particular attention in this project was focused on spore forming Bacillus species including the Sterne vaccine strain of Bacillus anthracis and the spore forming near-neighbor of Clostridium botulinum, C. novyi-NT. Bacillus species, including B. anthracis, the causative agent of inhalation anthrax are laboratory models for elucidating spore structure/function. Even though the complete genome sequence is available for B. subtilis, cereus, anthracis and other species, the determination and composition of spore structure/function is not understood. Prof. B. Vogelstein and colleagues at the John Hopkins University have recently developed a breakthrough bacteriolytic therapy for cancer treatment (1). They discovered that intravenously injected Clostridium novyi-NT spores germinate exclusively within the avascular regions of tumors in mice and destroy advanced cancerous lesions. The bacteria were also found to significantly improve the efficacy of chemotherapeutic drugs and radiotherapy (2,3). Currently, there is no understanding of the structure-function relationships of Clostridium novyi-NT spores. As well as their therapeutic interest, studies of Clostridium noyii spores could provide a model for further studies of human pathogenic spore formers including Clostridium botulinum and Clostridium perfringens. This project involved a multi-institutional collaboration of our LLNL group with the groups of Prof. T.J. Leighton (Children's Hospital Oakland Research Institute) and Prof. B. Vogelstein (The Howard Hughes Medical Institute and the Ludwig Center for Cancer Genetics and Therapeutics at The John Hopkins Sidney Kimmel Comprehensive Cancer Center)
SILAC-based phosphoproteomics reveals an inhibitory role of KSR1 in p53 transcriptional activity via modulation of DBC1
BACKGROUND
We have previously identified kinase suppressor of ras-1 (KSR1) as a potential regulatory gene in breast cancer. KSR1, originally described as a novel protein kinase, has a role in activation of mitogen-activated protein kinases. Emerging evidence has shown that KSR1 may have dual functions as an active kinase as well as a scaffold facilitating multiprotein complex assembly. Although efforts have been made to study the role of KSR1 in certain tumour types, its involvement in breast cancer remains unknown.
METHODS
A quantitative mass spectrometry analysis using stable isotope labelling of amino acids in cell culture (SILAC) was implemented to identify KSR1-regulated phosphoproteins in breast cancer. In vitro luciferase assays, co-immunoprecipitation as well as western blotting experiments were performed to further study the function of KSR1 in breast cancer.
RESULTS
Of significance, proteomic analysis reveals that KSR1 overexpression decreases deleted in breast cancer-1 (DBC1) phosphorylation. Furthermore, we show that KSR1 decreases the transcriptional activity of p53 by reducing the phosphorylation of DBC1, which leads to a reduced interaction of DBC1 with sirtuin-1 (SIRT1); this in turn enables SIRT1 to deacetylate p53.
CONCLUSION
Our findings integrate KSR1 into a network involving DBC1 and SIRT1, which results in the regulation of p53 acetylation and its transcriptional activity
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