generation-dispersed-presomitic-mesoderm-cell-cultures-for-imaging

Generation of Dispersed Presomitic Mesoderm Cell Cultures for Imaging of the Zebrafish Segmentation Clock in Single Cells

Segmentation is a  periodic and sequential morphogenetic process in vertebrates. This rhythmic  formation of blocks of tissue called somites along the ...

Max Planck Institute of Molecular Cell Biology and Genetics

In the fly Drosophila melanogaster, new genetic, physiological, molecular and behavioral techniques for the functional analysis of the brain are rapidly accumulating. These diverse investigations on the function of the insect brain use gene expression patterns that can be visualized and provide the means for manipulating groups of neurons as a common ground. To take advantage of these patterns one needs to know their typical anatomy.

The Virtual Insect Brain protocol: creating and comparing standardized neuroanatomy.

The peristimulus time histogram (PSTH) and the spike density function (SDF) are commonly used in the analysis of neurophysiological data. The PSTH is usually obtained by binning spike trains, the SDF being a (Gaussian) kernel smoothed version of the PSTH. While selection of the bin width or kernel size is often relatively arbitrary there have been recent attempts to remedy this situation (Shimazaki and Shinomoto, 2007c,b,a). We further develop an exact Bayesian generative model approach to estimating PSTHs (Endres et al., 2008) and demonstrate its superiority to competing methods using data from early (LGN) and late (STSa) visual areas. We also highlight the advantages of our scheme's automatic complexity control and generation of error bars. Additionally, our approach allows extraction of excitatory and inhibitory response latency from spike trains in a principled way, both on repeated and single trial data. We show that the method can be applied to data with high background firing rates and inhibitory responses (LGN) as well as to data with low firing rate and excitatory responses (STSa). Furthermore, we demonstrate on simulated data that our latency extraction method works for a range of signal-to-noise ratios and background firing rates. While further studies are needed to examine the sensitivity of our method to, for example, gradual changes in firing rate and adaptation, the current results suggest that Bayesian binning is a powerful method for the estimation of firing rate and the extraction response latency from neuronal spike trains.

Modelling spike trains and extracting response latency with Bayesian binning.

Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set.

A high-level 3D visualization API for Java and ImageJ.

Software for bead-based registration of selective plane illumination microscopy data.

The Drosophila brain is formed by an invariant set of lineages, each of which is derived from a unique neural stem cell (neuroblast) and forms a genetic and structural unit of the brain. The task of reconstructing brain circuitry at the level of individual neurons can be made significantly easier by assigning neurons to their respective lineages. In this article we address the automation of neuron and lineage identification. We focused on the Drosophila brain lineages at the larval stage when they form easily recognizable secondary axon tracts (SATs) that were previously partially characterized. We now generated an annotated digital database containing all lineage tracts reconstructed from five registered wild-type brains, at higher resolution and including some that were previously not characterized. We developed a method for SAT structural comparisons based on a dynamic programming approach akin to nucleotide sequence alignment and a machine learning classifier trained on the annotated database of reference SATs. We quantified the stereotypy of SATs by measuring the residual variability of aligned wild-type SATs. Next, we used our method for the identification of SATs within wild-type larval brains, and found it highly accurate (93-99%). The method proved highly robust for the identification of lineages in mutant brains and in brains that differed in developmental time or labeling. We describe for the first time an algorithm that quantifies neuronal projection stereotypy in the Drosophila brain and use the algorithm for automatic neuron and lineage recognition.

Identifying neuronal lineages of Drosophila by sequence analysis of axon tracts.

Eine zentrale Herausforderung in den Neurowissenschaften ist der rasche Wiederaufbau neuronale Schaltkreise. F&uuml;r Modellsysteme wie Drosophila und C. Elegans ist der limitierende Schritt nicht mehr den Erwerb der Bilder aber die Extraktion der Schaltung von Bildern. Zu diesem Zweck haben wir eine Software-Anwendung, TrakEM2, der systematischen Wiederaufbau der neuronalen Schaltkreise aus gro&szlig;en Elektron mikroskopische und optische Bildvolumen Adressen entwickelt. Wir begegnen die Herausforderungen der Band Bildkomposition aus einzelnen, deformierte Bilder; der Rekonstruktion der neuronalen Dorne und Annotation von Synapsen mit schnelle manuelle und halbautomatische Methoden; und die Verwaltung von gro&szlig;en Sammlungen von Bildern und Anmerkungen. Die Ausgabe ist ein Neuronaler Schaltkreis, der 3d Dorne und Synapsen, in NeuroML und anderen Formaten, bereit f&uuml;r die Analyse kodiert.

TrakEM2-Software f&uuml;r den Wiederaufbau der neuralen Schaltkreis.

Una sfida nelle neuroscienze &egrave; la rapida ricostruzione dei circuiti neuronali. Per i sistemi di modello come Drosophila e c. elegans, il passo limito non &egrave; pi&ugrave; l&#39;acquisizione di immagini, ma l&#39;estrazione del circuito da immagini. Per questo scopo, abbiamo progettato un software, TrakEM2, che affronta la ricostruzione sistematica dei circuiti neuronali da volumi di elettrone grande immagine microscopica e ottici. Affrontiamo le sfide della composizione volume immagine da immagini singole, deforme; la ricostruzione delle pergole neuronale e annotazione di sinapsi con metodi fast manuali e semiautomatici; e la gestione di grandi raccolte di immagini e di annotazioni. L&#39;output &egrave; un circuito neurale di pergole 3d e sinapsi, codificato in NeuroML ed in altri formati, pronti per l&#39;analisi.

TrakEM2 software per la ricostruzione del circuito neurale.

&#x91CD;&#x8981;&#x306A;&#x8AB2;&#x984C;&#x306E;&#x795E;&#x7D4C;&#x79D1;&#x5B66;&#x795E;&#x7D4C;&#x56DE;&#x8DEF;&#x306E;&#x8FC5;&#x901F;&#x306A;&#x518D;&#x69CB;&#x6210;&#x3067;&#x3059;&#x3002;&#x30B7;&#x30E7;&#x30A6;&#x30B8;&#x30E7;&#x30A6;&#x30D0;&#x30A8;&#x3001;&#x7DDA;&#x866B;&#x306A;&#x3069;&#x306E;&#x30E2;&#x30C7;&#x30EB; &#x30B7;&#x30B9;&#x30C6;&#x30E0;&#x306E;&#x5236;&#x9650;&#x306E;&#x30B9;&#x30C6;&#x30C3;&#x30D7;&#x306F;&#x3082;&#x306F;&#x3084;&#x753B;&#x50CF;&#x306E;&#x53D6;&#x5F97;&#x304C;&#x56DE;&#x8DEF;&#x306E;&#x62BD;&#x51FA;&#x753B;&#x50CF;&#x304B;&#x3089;&#x3067;&#x3059;&#x3002;&#x3053;&#x306E;&#x76EE;&#x7684;&#x306E;&#x305F;&#x3081;&#x306B;&#x3001;&#x30BD;&#x30D5;&#x30C8;&#x30A6;&#x30A7;&#x30A2; &#x30A2;&#x30D7;&#x30EA;&#x30B1;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x3001;&#x5927;&#x578B;&#x96FB;&#x5B50;&#x9855;&#x5FAE;&#x93E1;&#x304A;&#x3088;&#x3073;&#x5149;&#x5B66;&#x753B;&#x50CF;&#x30DC;&#x30EA;&#x30E5;&#x30FC;&#x30E0;&#x304B;&#x3089;&#x795E;&#x7D4C;&#x56DE;&#x8DEF;&#x7DB2;&#x306E;&#x4F53;&#x7CFB;&#x7684;&#x306A;&#x518D;&#x5EFA;&#x306E;&#x30A2;&#x30C9;&#x30EC;&#x30B9; TrakEM2 &#x306B;&#x8A2D;&#x8A08;&#x3055;&#x308C;&#x3066;&#x3002;&#x500B;&#x3005; &#x306E;&#x5909;&#x5F62;&#x3057;&#x305F;&#x753B;&#x50CF;&#x304B;&#x3089;&#x753B;&#x50CF;&#x30DC;&#x30EA;&#x30E5;&#x30FC;&#x30E0;&#x7D44;&#x6210;&#x3092;&#x8AB2;&#x984C;;&#x795E;&#x7D4C;&#x306E;&#x30A2;&#x30FC;&#x30D0;&#x30FC;&#x3068;&#x9AD8;&#x901F;&#x624B;&#x52D5;&#x304A;&#x3088;&#x3073;&#x534A;&#x81EA;&#x52D5;&#x30E1;&#x30BD;&#x30C3;&#x30C9;&#x3068;&#x30B7;&#x30CA;&#x30D7;&#x30B9;&#x306E;&#x30A2;&#x30CE;&#x30C6;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x306E;&#x5FA9;&#x8208;&#x306E;;&#x753B;&#x50CF;&#x3068;&#x6CE8;&#x91C8;&#x306E;&#x4E21;&#x65B9;&#x306E;&#x5927;&#x898F;&#x6A21;&#x306A;&#x30B3;&#x30EC;&#x30AF;&#x30B7;&#x30E7;&#x30F3;&#x306E;&#x7BA1;&#x7406;&#x3002;&#x51FA;&#x529B;&#x3059;&#x308B;&#x795E;&#x7D4C;&#x56DE;&#x8DEF; 3 d &#x30A2;&#x30FC;&#x30D0;&#x30FC;&#x306E;&#x30B7;&#x30CA;&#x30D7;&#x30B9;&#x3001;NeuroML &#x3068;&#x5206;&#x6790;&#x306E;&#x305F;&#x3081;&#x6E96;&#x5099;&#x304C;&#x3067;&#x304D;&#x3066;&#x3001;&#x4ED6;&#x306E;&#x5F62;&#x5F0F;&#x3067;&#x30A8;&#x30F3;&#x30B3;&#x30FC;&#x30C9;&#x3055;&#x308C;&#x307E;&#x3057;&#x305F;&#x3002;

&#x795E;&#x7D4C;&#x56DE;&#x8DEF;&#x306E;&#x518D;&#x69CB;&#x6210;&#x306E;&#x305F;&#x3081;&#x306E; TrakEM2 &#x30BD;&#x30D5;&#x30C8;&#x30A6;&#x30A7;&#x30A2;&#x3002;

&#x5728;&#x795E;&#x7ECF;&#x79D1;&#x5B66;&#x4E2D;&#x4E00;&#x4E2A;&#x5173;&#x952E;&#x7684;&#x6311;&#x6218;&#x662F;&#x8FC5;&#x901F;&#x91CD;&#x5EFA;&#x7684;&#x795E;&#x7ECF;&#x5143;&#x7535;&#x8DEF;&#x3002;&#x5BF9;&#x4E8E;&#x4F8B;&#x5982;&#x679C;&#x8747;&#x548C;&#x7EBF;&#x866B;&#x7684;&#x6A21;&#x578B;&#x7CFB;&#x7EDF;&#xFF0C;&#x9650;&#x5236;&#x4E00;&#x6B65;&#x4E0D;&#x518D;&#x662F;&#x56FE;&#x50CF;&#x7684;&#x91C7;&#x96C6;&#xFF0C;&#x4F46;&#x7535;&#x8DEF;&#x4ECE;&#x56FE;&#x50CF;&#x4E2D;&#x7684;&#x63D0;&#x53D6;&#x3002;&#x4E3A;&#x6B64;&#x76EE;&#x7684;&#xFF0C;&#x6211;&#x4EEC;&#x8BBE;&#x8BA1;&#x4E86;&#x4E00;&#x4E2A;&#x8F6F;&#x4EF6;&#x5E94;&#x7528;&#x7A0B;&#x5E8F;&#xFF0C;TrakEM2&#xFF0C;&#x4ECE;&#x5927;&#x578B;&#x7535;&#x5B50;&#x663E;&#x5FAE;&#x955C;&#x548C;&#x5149;&#x5B66;&#x56FE;&#x50CF;&#x5377;&#x6D89;&#x53CA;&#x7684;&#x795E;&#x7ECF;&#x5143;&#x7535;&#x8DEF;&#x7CFB;&#x7EDF;&#x91CD;&#x5EFA;&#x3002;&#x6211;&#x4EEC;&#x5904;&#x7406;&#x4ECE;&#x4E2A;&#x4EBA;&#x3001; &#x53D8;&#x5F62;&#x56FE;&#x50CF; &#xFF1B; &#x56FE;&#x50CF;&#x5377;&#x7EC4;&#x6210;&#x7684;&#x6311;&#x6218;&#x91CD;&#x5EFA;&#x7684;&#x795E;&#x7ECF;&#x5143;&#x4E54;&#x6728;&#x548C;&#x6CE8;&#x91CA;&#x7684;&#x795E;&#x7ECF;&#x7A81;&#x89E6;&#x4E0E;&#x5FEB;&#x901F;&#x624B;&#x52A8;&#x548C;&#x534A;&#x81EA;&#x52A8;&#x7684;&#x65B9;&#x6CD5; &#xFF1B;&#x548C;&#x7BA1;&#x7406;&#x7684;&#x5927;&#x578B;&#x96C6;&#x5408;&#x7684;&#x56FE;&#x50CF;&#x548C;&#x6279;&#x6CE8;&#x3002;&#x8F93;&#x51FA;&#x662F; 3d &#x4E54;&#x6728;&#x6811;&#x79CD;&#x7684;&#x795E;&#x7ECF;&#x56DE;&#x8DEF;&#x4E0E;&#x7A81;&#x89E6;&#x3001; &#x7F16;&#x7801;&#x7684; NeuroML &#x548C;&#x5176;&#x4ED6;&#x683C;&#x5F0F;&#xFF0C;&#x53EF;&#x4F9B;&#x5206;&#x6790;&#x3002;

&#x795E;&#x7ECF;&#x7535;&#x8DEF;&#x6539;&#x9020;&#x7684; TrakEM2 &#x8F6F;&#x4EF6;&#x3002;

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

TrakEM2 software for neural circuit reconstruction.

Un d&eacute;fi majeur en neurosciences est la reconstruction rapide des circuits neuronaux. Pour syst&egrave;mes de mod&egrave;les comme la drosophile et le c. elegans, l&#39;&eacute;tape limitante n&#39;est plus l&#39;acquisition d&#39;images, mais l&#39;extraction du circuit des images. &Agrave; cette fin, nous avons con&ccedil;u un logiciel, TrakEM2, qui porte sur la reconstruction syst&eacute;matique des circuits neuronaux de volumes image microscopique et optique large electron. Nous r&eacute;pondre aux d&eacute;fis de la composition d&#39;image volume d&#39;images individuelles, d&eacute;form&eacute;s ; de la reconstruction du neuronales tonnelles et annotation des synapses avec les m&eacute;thodes rapides manuelles et semi-automatiques ; et la gestion des grandes collections d&#39;images et annotations. La sortie est un circuit neuronal d&#39;axes 3d et synapses, encod&eacute; en NeuroML et d&#39;autres formats, pr&ecirc;ts pour l&#39;analyse.

Logiciel TrakEM2 pour la reconstruction des circuits neuronaux.

&#xC2E0;&#xACBD; &#xACFC;&#xD559;&#xC758; &#xD575;&#xC2EC; &#xACFC;&#xC81C;&#xB294; &#xC2E0;&#xACBD; &#xD68C;&#xB85C;&#xC758; &#xC2E0;&#xC18D;&#xD55C; &#xC7AC;&#xAC74; &#xC774;&#xB2E4;. C. elegans&#xC640; &#xCD08;&#xD30C;&#xB9AC; &#xBAA8;&#xB378; &#xC2DC;&#xC2A4;&#xD15C;&#xC744; &#xC704;&#xD55C; &#xC81C;&#xD55C; &#xB2E8;&#xACC4;&#xB294; &#xB354; &#xC774;&#xC0C1; &#xC774;&#xBBF8;&#xC9C0; &#xC218;&#xC9D1; &#xD558;&#xC9C0;&#xB9CC; &#xC774;&#xBBF8;&#xC9C0;&#xC5D0;&#xC11C; &#xD68C;&#xB85C; &#xCD94;&#xCD9C; &#xD569;&#xB2C8;&#xB2E4;. &#xC774; &#xBAA9;&#xC801;&#xC744; &#xC704;&#xD574; &#xC6B0;&#xB9AC;&#xB294; TrakEM2 &#xB300;&#xD615; &#xC804;&#xC790; &#xD604;&#xBBF8;&#xACBD; &#xBC0F; &#xAD11;&#xD559; &#xC774;&#xBBF8;&#xC9C0; &#xBCFC;&#xB968;&#xC5D0;&#xC11C; &#xC2E0;&#xACBD; &#xD68C;&#xB85C;&#xC758; &#xCCB4;&#xACC4;&#xC801;&#xC778; &#xC7AC;&#xAD6C;&#xC131;&#xC744; &#xD574;&#xACB0; &#xD558;&#xB294; &#xC18C;&#xD504;&#xD2B8;&#xC6E8;&#xC5B4; &#xC751;&#xC6A9; &#xD504;&#xB85C;&#xADF8;&#xB7A8;&#xC744; &#xC124;&#xACC4;. &#xC6B0;&#xB9AC; &#xAC1C;&#xC778;, &#xCC0C; &#xADF8;&#xB7EC; &#xC9C4; &#xC774;&#xBBF8;&#xC9C0;; &#xC774;&#xBBF8;&#xC9C0; &#xBCFC;&#xB968; &#xAD6C;&#xC131;&#xC758; &#xACFC;&#xC81C;&#xB97C; &#xD574;&#xACB0; &#xC2E0;&#xACBD; &#xC544; &#xBC84; &#xBC0F; &#xBE60;&#xB978; &#xC218;&#xB3D9; &#xBC0F; &#xBC18;&#xC790;&#xB3D9; &#xBC29;&#xBC95;; &#xB0C5;&#xC758; &#xC8FC;&#xC11D; &#xC7AC;&#xAC74;&#xC758; &#xADF8;&#xB9AC;&#xACE0; &#xC774;&#xBBF8;&#xC9C0;&#xC640; &#xC8FC;&#xC11D;&#xC744; &#xB300;&#xD615; &#xCEEC;&#xB809;&#xC158; &#xAD00;&#xB9AC;. &#xCD9C;&#xB825; 3d &#xC544; &#xBC84;&#xC758; &#xC2E0;&#xACBD; &#xD68C;&#xB85C; synapses, NeuroML &#xBC0F; &#xBD84;&#xC11D;&#xC5D0; &#xB300; &#xD55C; &#xC900;&#xBE44;, &#xB2E4;&#xB978; &#xD615;&#xC2DD;&#xC73C;&#xB85C; &#xC778;&#xCF54;&#xB529;&#xB41C;.

&#xC2E0;&#xACBD; &#xD68C;&#xB85C; &#xC7AC;&#xAD6C;&#xC131;&#xC5D0; &#xB300; &#xD55C; TrakEM2 &#xC18C;&#xD504;&#xD2B8;&#xC6E8;&#xC5B4;&#xC785;&#xB2C8;&#xB2E4;.

Un reto fundamental en Neurociencias es la r&aacute;pida reconstrucci&oacute;n de circuitos neuronales. Para sistemas de modelo Drosophila como Caenorhabditis elegans, el paso limitante ya no es la adquisici&oacute;n de im&aacute;genes pero la extracci&oacute;n del circuito de im&aacute;genes. Para ello, hemos dise&ntilde;ado una aplicaci&oacute;n de software, TrakEM2, que aborda la reconstrucci&oacute;n sistem&aacute;tica de circuitos neuronales de vol&uacute;menes de imagen microsc&oacute;pica y &oacute;ptica de electr&oacute;n grande. Abordamos los desaf&iacute;os de la composici&oacute;n de imagen volumen de im&aacute;genes individuales, deformadas; de la reconstrucci&oacute;n de cenadores neuronales y anotaci&oacute;n de sinapsis con m&eacute;todos r&aacute;pido manuales y semiautom&aacute;ticas; y la gesti&oacute;n de grandes colecciones de im&aacute;genes y anotaciones. La salida es un circuito neural de cenadores 3d y sinapsis, codificados en NeuroML y otros formatos, listos para el an&aacute;lisis.

TrakEM2 software para la reconstrucci&oacute;n de circuito neural.

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

Fiji: an open-source platform for biological-image analysis.

OpenSPIM: an open-access light-sheet microscopy platform.

During embryonic development, temporal and spatial cues are coordinated to generate a segmented body axis. In sequentially segmenting animals, the rhythm of segmentation is reported to be controlled by the time scale of genetic oscillations that periodically trigger new segment formation. However, we present real-time measurements of genetic oscillations in zebrafish embryos showing that their time scale is not sufficient to explain the temporal period of segmentation. A second time scale, the rate of tissue shortening, contributes to the period of segmentation through a Doppler effect. This contribution is modulated by a gradual change in the oscillation profile across the tissue. We conclude that the rhythm of segmentation is an emergent property controlled by the time scale of genetic oscillations, the change of oscillation profile, and tissue shortening.

Johannes Schindelin

Max Planck Institute of Molecular Cell Biology and Genetics

Generation of Dispersed Presomitic Mesoderm Cell Cultures for Imaging of the Zebrafish Segmentation Clock in Single Cells