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214 Publications
Showing 201-210 of 214 resultsAlthough myosin II filaments are known to exist in non-muscle cells, their dynamics and organization are incompletely understood. Here, we combined structured illumination microscopy with pharmacological and genetic perturbations, to study the process of actomyosin cytoskeleton self-organization into arcs and stress fibres. A striking feature of the myosin II filament organization was their 'registered' alignment into stacks, spanning up to several micrometres in the direction orthogonal to the parallel actin bundles. While turnover of individual myosin II filaments was fast (characteristic half-life time 60 s) and independent of actin filament turnover, the process of stack formation lasted a longer time (in the range of several minutes) and required myosin II contractility, as well as actin filament assembly/disassembly and crosslinking (dependent on formin Fmnl3, cofilin1 and α-actinin-4). Furthermore, myosin filament stack formation involved long-range movements of individual myosin filaments towards each other suggesting the existence of attractive forces between myosin II filaments. These forces, possibly transmitted via mechanical deformations of the intervening actin filament network, may in turn remodel the actomyosin cytoskeleton and drive its self-organization.
A complex brain consists of multiple intricate neural networks assembled from distinct sets of input and output neurons as well as region-specific local interneurons. Within a given anatomical set, there exist diverse neuronal types that can vary in morphology, neural physiology, and modes of neurotransmission. The genetic programs that guide specification of neuronal types during neurogenesis preconfigure the brain. This is best demonstrated in the Drosophila central brain, which is composed of ∼100 pairs of individually tailored neuronal lineages. Each neuronal lineage (the neurons/glia produced from a single stem cell) can contain multiple morphological classes of neurons that can consist of many analogous neuronal types. The detailed patterns of neuronal diversification are lineage-specific and can differ drastically even among neighboring neuronal lineages. Furthermore, the interrelationships between neuronal lineages and neural networks are complex. These phenomena underscore the importance of tracking all neuronal lineages in understanding brain development and evolution.
Extracellular expression of heat shock protein 90 (eHsp90) by tumor cells is correlated with malignancy. Development of small molecule probes that can detect eHsp90 in vivo may therefore have utility in the early detection of malignancy. We synthesized a cell impermeable far-red fluorophore-tagged Hsp90 inhibitor to target eHsp90 in vivo. High resolution confocal and lattice light sheet microscopy show that probe-bound eHsp90 accumulates in punctate structures on the plasma membrane of breast tumor cells and is actively internalized. The extent of internalization correlates with tumor cell aggressiveness, and this process can be induced in benign cells by over-expressing p110HER2. Whole body cryoslicing, imaging and histology of flank and spontaneous tumor-bearing mice strongly suggests that eHsp90 expression and internalization is a phenomenon unique to tumor cells in vivo and may provide an 'Achilles heel' for the early diagnosis of metastatic disease and targeted drug delivery.
Fluorescent proteins (FPs) that can be optically highlighted enable PALM super-resolution microscopy and pulse-chase experiments of cellular molecules. Most FPs evolved in cytoplasmic environments either in the original source organism or in the cytoplasm of bacteria during the course of optimization for research applications. Consequently, many FPs may fold incorrectly in the chemically distinct environments in subcellular organelles. Here, we describe the first monomeric photoswitchable (from green to bright red) FP adapted for oxidizing environments.
Electrical recordings from a large array of electrodes give us access to neural population activity with single-cell, single-spike resolution. These recordings contain extracellular spikes which must be correctly detected and assigned to individual neurons. Despite numerous spike-sorting techniques developed in the past, a lack of high-quality ground-truth datasets hinders the validation of spike-sorting approaches. Furthermore, existing approaches requiring manual corrections are not scalable for hours of recordings exceeding 100 channels. To address these issues, we built a comprehensive spike-sorting pipeline that performs reliably under noise and probe drift by incorporating a channel-covariance feature and a clustering based on fast density-peak finding. We validated performance of our workflow using multiple ground-truth datasets that recently became available. Our software scales linearly and processes a 1000-channel recording in real-time using a single workstation. Accurate, real-time spike sorting from large recording arrays will enable more precise control of closed-loop feedback experiments and brain-computer interfaces.
In this paper, we present a novel error measure to compare a computer-generated segmentation of images or volumes against ground truth. This measure, which we call Tolerant Edit Distance (TED), is motivated by two observations that we usually encounter in biomedical image processing: (1) Some errors, like small boundary shifts, are tolerable in practice. Which errors are tolerable is application dependent and should be explicitly expressible in the measure. (2) Non-tolerable errors have to be corrected manually. The effort needed to do so should be reflected by the error measure. Our measure is the minimal weighted sum of split and merge operations to apply to one segmentation such that it resembles another segmentation within specified tolerance bounds. This is in contrast to other commonly used measures like Rand index or variation of information, which integrate small, but tolerable, differences. Additionally, the TED provides intuitive numbers and allows the localization and classification of errors in images or volumes. We demonstrate the applicability of the TED on 3D segmentations of neurons in electron microscopy images where topological correctness is arguable more important than exact boundary locations. Furthermore, we show that the TED is not just limited to evaluation tasks. We use it as the loss function in a max-margin learning framework to find parameters of an automatic neuron segmentation algorithm. We show that training to minimize the TED, i.e., to minimize crucial errors, leads to higher segmentation accuracy compared to other learning methods.
The delivery of tracers into populations of neurons is essential to visualize their anatomy and analyze their function. In some model systems genetically-targeted expression of fluorescent proteins is the method of choice; however, these genetic tools are not available for most organisms and alternative labeling methods are very limited. Here we describe a new method for neuronal labelling by electrophoretic dye delivery from a suction electrode directly through the neuronal sheath of nerves and ganglia in insects. Polar tracer molecules were delivered into the locust auditory nerve without destroying its function, simultaneously staining peripheral sensory structures and central axonal projections. Local neuron populations could be labelled directly through the surface of the brain, and in-vivo optical imaging of sound-evoked activity was achieved through the electrophoretic delivery of calcium indicators. The method provides a new tool for studying how stimuli are processed in peripheral and central sensory pathways and is a significant advance for the study of nervous systems in non-model organisms.
Although purification of biotinylated molecules is highly efficient, identifying specific sites of biotinylation remains challenging. We show that anti-biotin antibodies enable unprecedented enrichment of biotinylated peptides from complex peptide mixtures. Live-cell proximity labeling using APEX peroxidase followed by anti-biotin enrichment and mass spectrometry yielded over 1,600 biotinylation sites on hundreds of proteins, an increase of more than 30-fold in the number of biotinylation sites identified compared to streptavidin-based enrichment of proteins.
Populations of neurons in primary visual cortex (V1) transform direct thalamic inputs into a cortical representation which acquires new spatio-temporal properties. One of these properties, motion selectivity, has not been strongly tied to putative neural mechanisms, and its origins remain poorly understood. Here we propose that motion selectivity is acquired through the recurrent mechanisms of a network of strongly connected neurons. We first show that a bank of V1 spatiotemporal receptive fields can be generated accurately by a network which receives only instantaneous inputs from the retina. The temporal structure of the receptive fields is generated by the long timescale dynamics associated with the high magnitude eigenvalues of the recurrent connectivity matrix. When these eigenvalues have complex parts, they generate receptive fields that are inseparable in time and space, such as those tuned to motion direction. We also show that the recurrent connectivity patterns can be learnt directly from the statistics of natural movies using a temporally-asymmetric Hebbian learning rule. Probed with drifting grating stimuli and moving bars, neurons in the model show patterns of responses analogous to those of direction-selective simple cells in primary visual cortex. These computations are enabled by a specific pattern of recurrent connections, that can be tested by combining connectome reconstructions with functional recordings.
Regions of genomic DNA called enhancers encode binding sites for transcription factor proteins. Binding of activators and repressors increase and reduce transcription, respectively, but it is not understood how combinations of activators and repressors generate precise patterns of transcription during development. Here, we explore this problem using a fully synthetic transcriptional platform in Drosophila consisting of engineered transcription factor gradients and artificial enhancers. We found that binding sites for a transcription factor that makes DNA accessible are required together with binding sites for transcriptional activators to produce a functional enhancer. Only in this context can changes in the number of activator binding sites mediate quantitative control of transcription. Using an engineered transcriptional repressor gradient, we demonstrate that overlapping repressor and activator binding sites provide more robust repression and sharper expression boundaries than non-overlapping sites. This may explain why this common motif is observed in many developmental enhancers.