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Use of independent component analysis to improve signal-to-noise ratio in multi-probe fluorescence microscopy.

ABSTRACT: In conventional multi-probe fluorescence microscopy, narrow bandwidth filters on detectors are used to avoid bleed-through artefacts between probes. The limited bandwidth reduces the signal-to-noise ratio of the detection, often severely compromising one or more channels. Herein, we describe a process of using independent component analysis to discriminate the position of different probes using only a dichroic mirror to differentiate the signals directed to the detectors. Independent component analysis was particularly effective in samples where the spatial overlap between the probes is minimal, a very common case in cellular microscopy. This imaging scheme collects nearly all of the emitted light, significantly improving the image signal-to-noise ratio. In this study, we focused on the detection of two fluorescence probes used in vivo, NAD(P)H and ANEPPS. The optimal dichroic mirror cutoff frequency was determined with simulations using the probes spectral emissions. A quality factor, defined as the cross-channel contrast-to-noise ratio, was optimized to maximize signals while maintaining spatial discrimination between the probes after independent component analysis post-processing. Simulations indicate that a ?3 fold increase in signal-to-noise ratio using the independent component analysis approach can be achieved over the conventional narrow-band filtering approach without loss of spatial discrimination. We confirmed this predicted performance from experimental imaging of NAD(P)H and ANEPPS in mouse skeletal muscle, in vivo. For many multi-probe studies, the increased sensitivity of this 'full bandwidth' approach will lead to improved image quality and/or reduced excitation power requirements.

SUBMITTER: Dao L

PROVIDER: S-EPMC4229364 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Project description:MotivationTarget-specific hybridization depends on oligo-probe characteristics that improve hybridization specificity and minimize genome-wide cross-hybridization. Interplay between specific hybridization and genome-wide cross-hybridization has been insufficiently studied, despite its crucial role in efficient probe design and in data analysis.ResultsIn this study, we defined hybridization specificity as a ratio between oligo target-specific hybridization and oligo genome-wide cross-hybridization. A microarray database, derived from the Genomic Comparison Hybridization (GCH) experiment and performed using the Affymetrix platform, contains two different types of probes. The first type of oligo-probes does not have a specific target on the genome and their hybridization signals are derived from genome-wide cross-hybridization alone. The second type includes oligonucleotides that have a specific target on the genomic DNA and their signals are derived from specific and cross-hybridization components combined together in a total signal. A comparative analysis of hybridization specificity of oligo-probes, as well as their nucleotide sequences and thermodynamic features was performed on the database. The comparison has revealed that hybridization specificity was negatively affected by low stability of the fully-paired oligo-target duplex, stable probe self-folding, G-rich content, including GGG motifs, low sequence complexity and nucleotide composition symmetry.ConclusionFiltering out the probes with defined 'negative' characteristics significantly increases specific hybridization and dramatically decreasing genome-wide cross-hybridization. Selected oligo-probes have two times higher hybridization specificity on average, compared to the probes that were filtered from the analysis by applying suggested cutoff thresholds to the described parameters. A new approach for efficient oligo-probe design is described in our study.Contactshabalin@ncbi.nlm.nih.gov or olga.matveeva@gmail.comSupplementary informationSupplementary data are available at Bioinformatics online.

Project description:It is well established that non-uniform sampling (NUS) allows acquisition of multi-dimensional NMR spectra at a resolution that cannot be obtained with traditional uniform acquisition through the indirect dimensions. However, the impact of NUS on the signal-to-noise ratio (SNR) and sensitivity are less well documented. SNR and sensitivity are essential aspects of NMR experiments as they define the quality and extent of data that can be obtained. This is particularly important for spectroscopy with low concentration samples of biological macromolecules. There are different ways of defining the SNR depending on how to measure the noise, and the distinction between SNR and sensitivity is often not clear. While there are defined procedures for measuring sensitivity with high concentration NMR standards, such as sucrose, there is no clear or generally accepted definition of sensitivity when comparing different acquisition and processing methods for spectra of biological macromolecules with many weak signals close to the level of noise. Here we propose tools for estimating the SNR and sensitivity of NUS spectra with respect to sampling schedule and reconstruction method. We compare uniformly acquired spectra with NUS spectra obtained in the same total measuring time. The time saving obtained when only 1/k of the Nyquist grid points are sampled is used to measure k-fold more scans per increment. We show that judiciously chosen NUS schedules together with suitable reconstruction methods can yield a significant increase of the SNR within the same total measurement time. Furthermore, we propose to define the sensitivity as the probability to detect weak peaks and show that time-equivalent NUS can considerably increase this detection sensitivity. The sensitivity gain increases with the number of NUS indirect dimensions. Thus, well-chosen NUS schedules and reconstruction methods can significantly increase the information content of multidimensional NMR spectra of challenging biological macromolecules.

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Use of independent component analysis to improve signal-to-noise ratio in multi-probe fluorescence microscopy.

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