42 research outputs found
Robust Hypothesis Tests for Detecting Statistical Evidence of 2D and 3D Interactions in Single-Molecule Measurements
A variety of experimental techniques have improved the 2D and 3D spatial
resolution that can be extracted from \emph{in vivo} single-molecule
measurements. This enables researchers to quantitatively infer the magnitude
and directionality of forces experienced by biomolecules in their native
cellular environments. Situations where such forces are biologically relevant
range from mitosis to directed transport of protein cargo along cytoskeletal
structures. Models commonly applied to quantify single-molecule dynamics assume
that effective forces and velocity in the (or ) directions are
statistically independent, but this assumption is physically unrealistic in
many situations. We present a hypothesis testing approach capable of
determining if there is evidence of statistical dependence between positional
coordinates in experimentally measured trajectories; if the hypothesis of
independence between spatial coordinates is rejected, then a new model
accounting for 2D (3D) interactions should be considered to more faithfully
represent the underlying experimental kinetics. The technique is robust in the
sense that 2D (3D) interactions can be detected via statistical hypothesis
testing even if there is substantial inconsistency between the physical
particle's actual noise sources and the simplified model's assumed noise
structure. For example, 2D (3D) interactions can be reliably detected even if
the researcher assumes normal diffusion, but the experimental data experiences
"anomalous diffusion" and/or is subjected to a measurement noise characterized
by a distribution differing from that assumed by the fitted model. The approach
is demonstrated on control simulations and on experimental data (IFT88 directed
transport in the primary cilium).Comment: 7 pages, 6 figure
Deep-STORM: super-resolution single-molecule microscopy by deep learning
We present an ultra-fast, precise, parameter-free method, which we term
Deep-STORM, for obtaining super-resolution images from stochastically-blinking
emitters, such as fluorescent molecules used for localization microscopy.
Deep-STORM uses a deep convolutional neural network that can be trained on
simulated data or experimental measurements, both of which are demonstrated.
The method achieves state-of-the-art resolution under challenging
signal-to-noise conditions and high emitter densities, and is significantly
faster than existing approaches. Additionally, no prior information on the
shape of the underlying structure is required, making the method applicable to
any blinking data-set. We validate our approach by super-resolution image
reconstruction of simulated and experimentally obtained data.Comment: 7 pages, added code download reference and DOI for the journal
versio
DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learning
Localization microscopy is an imaging technique in which the positions of
individual nanoscale point emitters (e.g. fluorescent molecules) are determined
at high precision from their images. This is the key ingredient in
single/multiple-particle-tracking and several super-resolution microscopy
approaches. Localization in three-dimensions (3D) can be performed by modifying
the image that a point-source creates on the camera, namely, the point-spread
function (PSF). The PSF is engineered using additional optical elements to vary
distinctively with the depth of the point-source. However, localizing multiple
adjacent emitters in 3D poses a significant algorithmic challenge, due to the
lateral overlap of their PSFs. Here, we train a neural network to receive an
image containing densely overlapping PSFs of multiple emitters over a large
axial range and output a list of their 3D positions. Furthermore, we then use
the network to design the optimal PSF for the multi-emitter case. We
demonstrate our approach numerically as well as experimentally by 3D STORM
imaging of mitochondria, and volumetric imaging of dozens of
fluorescently-labeled telomeres occupying a mammalian nucleus in a single
snapshot.Comment: main text: 9 pages, 5 figures, supplementary information: 29 pages,
20 figure
Genome sequence of the tsetse fly (Glossina morsitans):Vector of African trypanosomiasis
Tsetse flies are the sole vectors of human African trypanosomiasis throughout sub-Saharan Africa.
Both sexes of adult tsetse feed exclusively on blood and contribute to disease transmission. Notable
differences between tsetse and other disease vectors include obligate microbial symbioses, viviparous
reproduction, and lactation. Here, we describe the sequence and annotation of the 366-megabase
Glossina morsitans morsitans genome. Analysis of the genome and the 12,308 predicted
protein-encoding genes led to multiple discoveries, including chromosomal integrations of bacterial
(Wolbachia) genome sequences, a family of lactation-specific proteins, reduced complement of
host pathogen recognition proteins, and reduced olfaction/chemosensory associated genes. These
genome data provide a foundation for research into trypanosomiasis prevention and yield important
insights with broad implications for multiple aspects of tsetse biology.IS