Maximizing the biochemical resolving power of fluorescence microscopy.

Most recent advances in fluorescence microscopy have focused on achieving spatial resolutions below the diffraction limit. However, the inherent capability of fluorescence microscopy to non-invasively resolve different biochemical or physical environments in biological samples has not yet been formally described, because an adequate and general theoretical framework is lacking. Here, we develop a mathematical characterization of the biochemical resolution in fluorescence detection with Fisher information analysis. To improve the precision and the resolution of quantitative imaging methods, we demonstrate strategies for the optimization of fluorescence lifetime, fluorescence anisotropy and hyperspectral detection, as well as different multi-dimensional techniques. We describe optimized imaging protocols, provide optimization algorithms and describe precision and resolving power in biochemical imaging thanks to the analysis of the general properties of Fisher information in fluorescence detection. These strategies enable the optimal use of the information content available within the limited photon-budget typically available in fluorescence microscopy. This theoretical foundation leads to a generalized strategy for the optimization of multi-dimensional optical detection, and demonstrates how the parallel detection of all properties of fluorescence can maximize the biochemical resolving power of fluorescence microscopy, an approach we term Hyper Dimensional Imaging Microscopy (HDIM). Our work provides a theoretical framework for the description of the biochemical resolution in fluorescence microscopy, irrespective of spatial resolution, and for the development of a new class of microscopes that exploit multi-parametric detection systems

Fast and simple spectral FLIM for biochemical and medical imaging.

Spectrally resolved fluorescence lifetime imaging microscopy (λFLIM) has powerful potential for biochemical and medical imaging applications. However, long acquisition times, low spectral resolution and complexity of λFLIM often narrow its use to specialized laboratories. Therefore, we demonstrate here a simple spectral FLIM based on a solid-state detector array providing in-pixel histrogramming and delivering faster acquisition, larger dynamic range, and higher spectral elements than state-of-the-art λFLIM. We successfully apply this novel microscopy system to biochemical and medical imaging demonstrating that solid-state detectors are a key strategic technology to enable complex assays in biomedical laboratories and the clinic.A.E. thanks the EPSRC for the initial funding of the project (EP/F044011/1) from 2009 to 2011. M.P. and L.D.C. were supported by a Programme Grant to A.R.V. from the UK Medical Research Council (MRC). This project was also supported by the MRC’s grant-in-aid to the Cancer Unit, Cambridge (A.E., A.R.V.). C.F.K acknowledges funding from the MRC (grant MR/K015850/1), the Wellcome Trust (grant 089703/Z/09/Z) and the EPSRC (EP/L015889/1).This is the author accepted manuscript. The final version is available from the Optical Society of America via http://dx.doi.org/10.1364/OE.23.02351

Spectral unmixing of fluorescent proteins.

<p>True color images of cells expressing EGFP-Actin (<b>a</b>), EYFP-Tubulin (<b>b</b>), both proteins (<b>c</b>) and total photon count per pixel (<b>d</b>) for the latter. Spectral unmixing was used for different gating schemes (<b>e</b>–<b>j</b>) for gates of even width with 3 (<b>e</b>), 9 (<b>f</b>) and 34 (<b>g</b>) gates and for optimized gates of 2 (<b>h</b>), 4 (<b>i</b>) and 8 (<b>j</b>) gates. The spectra gating scheme is displayed at the bottom of the panels with the reference spectra (<b>k</b>) overlaid for comparison. In green the signal recovered for GFP-Actin and in red the signal estimated for YFP-Tubulin. The comparison of the best achievable F-values measured experimentally demonstrate the positive impact of gating optimization and the trend to provide more precise results at increasing channel number (<b>l</b>).</p

Optimization of time gates.

<p>A set of three typical Monte Carlo simulations (<b>a</b>) ran with different photon counts (cyan: 10,000, red: 1,000 and green 250) to evaluate the performance of the system (F-value) as a function of the position of a time gate for a two gate lifetime detection system. The minima of the curves (the best performance) match the theoretical values. At low photon counts (green curve and arrow) the results deviate by the others for high values of gate timings because of the presence of bias caused by too low photon counts in the second gate. The F-value is also estimated numerically by the direct estimation of Fisher information (<b>b</b>). Here the blue curve is the same of panel <b>a</b> and it is plotted for reference. <b>c</b>) The best position of the time gates (red circles in <b>b</b> and <b>c</b>) is also plotted as a function of the simulated fluorescence lifetimes (from 0.4 to 4.0 ns). The slope of the line identifies the best position of the second gate matching the predictions illustrated in panel <b>a</b>. Numerical optimization of Fisher information for a system with 4 (<b>d</b>) and 8 (<b>e</b>) gates are also shown; the -value for 4 or 8 gates is plotted for gates of equal width (blue), for gates optimized by (red) followed by (green) and for a reference partition over 256 time gates (black, representative of TCSPC). The vertical lines delimit the regions within which the optimization algorithms were used. The time gating scheme is represented by the boxes at the bottom of panels <b>d</b> and <b>e</b>. The efficiency of the optimization strategies relative to TCSPC is also plotted <i>versus</i> the number of gates (<b>f</b>) for gates of equal width (blue, “even gates”), for gates optimized by (red, “optimization”) and then followed by (green, “full optimization”).</p

Biochemical resolving power.

<p>Panels <b>a</b>–<b>e</b> show the numerical estimations of the F-value of the same optical signatures used in Fig. 4 at increasing spectral shift for FLIM (dashed blue curve), spectral imaging (red), anisotropy imaging (green), SLIM (fuchsia), time resolved anisotropy (dashed cyan curve), spectrally resolved anisotropy (dashed yellow curve) and HDIM (black). Spectral imaging does not achieve F-values in the regions plotted in panel <b>a</b> and <b>b</b> because of the small spectral shifts considered (0 nm and 5 nm, respectively). Panel <b>c</b> shows also the minimum -values for each technique: <i>e.g.</i>, with ∼2.18, HDIM unmixed the signatures of the two fluorophores with a relative error of ∼5% if at least 1,900 photons/pixel are collected. Spectral imaging alone requires ∼(2.87/2.18)² times more photons (∼3,300) to achieve the same result.</p

Fast and simple spectral FLIM for biochemical and medical imaging

Spectrally resolved fluorescence lifetime imaging microscopy (λFLIM) has powerful potential for biochemical and medical imaging applications. However, long acquisition times, low spectral resolution and complexity of λFLIM often narrow its use to specialized laboratories. Therefore, we demonstrate here a simple spectral FLIM based on a solid-state detector array providing in-pixel histrogramming and delivering faster acquisition, larger dynamic range, and higher spectral elements than state-of-the-art λFLIM. We successfully apply this novel microscopy system to biochemical and medical imaging demonstrating that solid-state detectors are a key strategic technology to enable complex assays in biomedical laboratories and the clinic

ELIXIR-CONVERGE D5.4 Report on KPI

The overall goal of ELIXIR-CONVERGE&rsquo;s WP5 was, based on the analysis of a set of very diverse use cases, to contribute to the development of a set of resources supporting the development and implementation of Data Management Plans (DMPs) in national and transnational projects. Six use cases were considered to co-develop and test a method to address domain specific data management planning associated to a set of resources in collaboration with WP1, WP2 and WP3. In this context, another objective of WP5 was to develop, implement and refine key performance indicators (KPIs) in order to monitor the demonstrator projects&rsquo; implementation of data management plans and possibly assess their adoption by the relevant community. WP5 developed two sets KPIs in collaboration with WP4, in charge of developing KPIs across the entire ELIXIR-CONVERGE project and for assessing the impact of the project : a first set aiming at monitoring the development of guidance, resources for the development of DMP and for training in the context of the use cases a second set aiming at addressing the adoption of these resources by relevant communities of users as a way to assess the impact of the work achieved In parallel, in order to ensure long term maintenance/update of the developed resources and to increase their impact, WP5 started to engage with ELIXIR communities that could be natural owners of these resources. The KPI developed and collected by WP5 during the ELIXIR-CONVERGE project were useful to follow the partner&rsquo;s progress in the development and test of a sort of starter kit for domain specific data management support. Success stories could be collected showing adoption by communities and new projects and were mapped on ELIXIR&rsquo;s categories of impacts showing already &ldquo;hits&rdquo; on several of these.</p

ELIXIR-CONVERGE D5.3 Report on the dedicated training and capacity building activities

One of the objectives of WP5 was to identify the needs in terms of capacity building, in the context of the 6 use cases, and to develop strategy(ies) in collaboration with WP2 to address these needs. This was achieved in three steps: Create an inventory of training resources available in the context of the use cases&nbsp; Send an online survey followed&nbsp; by a series of F2F meetings with each use case to collect their training needs Test the development of learning paths as a method to develop a structured overview of training needs in relation to a use case&nbsp; The method set up by the ELIXIR Training Platform to better structure the development of training resources was proved very helpful to specify the needs in terms of training and capacity building in the context of the use cases. Spelling out the priority in terms of training target and the learning path to follow to reach this target is a way to detail the different modules that are necessary. It is then possible to map on these modules the training resources that are already available and to derive a list of gaps. This method was applied for three demo cases successfully in the Plant, Marine metagenomics and Human Genome data contexts.&nbsp; In terms of general organisation of community resources to facilitate this type of approach, the most important actions are to make the training materials accessible for reuse and easy to find. Versioning is highly desirable, to facilitate collaborative development and maintenance of training material&nbsp; of the time and a DOI attribution will facilitate citation and long term findability. Findability requires additional work to organise the training events and training resources in categories and/or facilitate filtering in the context of catalogues of training resources such as TeSS or Glittr or of knowledge hubs such as the RDMkit. This is achieved in TeSS, through the annotation of the training material and linked training events with ontology terms from EDAM, as well as with custom keywords.&nbsp; &nbsp;</p