ASTRAL, a hyperspectral imaging DNA sequencer

O’Brien, Kevin; Wren, Jonathan D.; Bai, Diane; Anderson, Richard; Rayner, Simon; Evans, Glen A.; Dabiri, A.E.; Garner, Harold R.

doi:10.1063/1.1148913

Cited by 12 publications

(9 citation statements)

References 5 publications

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“…Both techniques employed the sensor response profiles extracted from the CCD-based image stacks, essentially creating three-dimensions of information for sensor discrimination -odor exposure (spatial, time, intensity) and emission scan (spatial, spectral, intensity). These types of data are similar to data recorded with hyperspectral imagers or spectrograph-CCD combinations [32]. In our work the emission scan served as a complementary decoding technique to the odor response information.…”

Section: Homogeneous Single-sensor Arraysmentioning

confidence: 99%

“…Recent studies have documented, or made reference to, optical decoding processes. These processes include quantum dots [24], fluorescent colloids [25,26], metallic barcodes [27], flow cytometry [28,29], protein-and cell-based assays [11,30], multiplexed assays [3,10,31], and spatial and spectral imaging [32,33,34], further signifying the importance of optical decoding techniques for a broad range of detection applications.…”

Section: Introductionmentioning

confidence: 99%

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Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays

et al. 2002

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In this paper automatic sensor identification of sensor classes within a high-density randomized array, without a priori knowledge of sensor locations, is demonstrated. Two different fluorescence-based sensor types, with hundreds of replicates each, were randomly distributed into an optical imaging fiber array platform. The sensor element types were vapor-sensitive microspheres with the environmentally-sensitive fluorescent dye Nile Red adsorbed on their surface. Nile Red undergoes spectral changes when exposed to different microenvironmental polarity conditions, e.g. microsphere surface polarity or odor exposure. These reproducible sensor spectral changes, or sensor-response profiles, enable sensors within a randomized array to be grouped into categories by optical decoding methods. Two computational decoding methods (supervised and unsupervised) are introduced; equal classification rates were achieved for both. By comparing sensor responses from a randomized array with those obtained from known (control) arrays, 587 sensors were correctly classified with 99.32% accuracy. Although both methods were equally effective, the unsupervised method, which uses sensor response changes to odor exposure, is a better decoding model for the vapor-sensitive arrays studied, because it relies only on the odor-response profiles. Another decoding technique employed the emission spectra of the sensors and is more applicable to other types of multiplexed fluorescence-based arrays and assays. The sensor-decoding techniques are compared to demonstrate that sensors within high-density optical chemosensor arrays can be positionally-registered, or decoded, with no additional overhead in time or expense other than collecting the sensor-response profiles.

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Section: Homogeneous Single-sensor Arraysmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays

et al. 2002

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“…A similar strategy is used in flow cytometric detection of particular proteins [26]. However, prototype automatic analysis of polyacrylamide slab gels imaged through a novel optical detection system has recently been developed [27]. The design of this prototype sequencer allows direct optical coupling over the entire read area of the gel and spectrographic separation and detection of the fluorescence emission.…”

Section: Introductionmentioning

confidence: 99%

Histometrics: Improvement of the dynamic range of fluorescently stained proteins resolved in electrophoretic gels using hyperspectral imaging

Woodward

Kaderbhai

et al. 2001

Proteomics

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Most image-based analyses, using absorbance or fluorescence of the spatial distribution of identifiable structures in complex biological systems, use only a very small number of dimensions of possible spectral data for the generation and interpretation of the image. We here extend the concepts of hyperspectral imaging, being developed in remote sensing, into analytical biotechnology. The massive volume of information contained in hyperspectral spectroscopic images requires multivariate analysis in order to extract the chemical and spatial information contained within the data. We here describe the use of multivariate statistical methods to map and quantify common protein staining fluorophores (SYPRO Red, Orange and Tangerine) in electrophoretic gels. Specifically, we find (a) that the 'background' underpinning limits of detection is due more to proteins that have not migrated properly than to impurities or to ineffective destaining, (b) the detailed mechanisms of staining of SYPRO red and orange are apparently not identical, and in particular (c) that these methods can provide two orders of magnitude improvement in the detection limit per pixel, to levels well below the limit observable optically.

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“…To achieve higher throughput than current stateof-the-art sequencers, a hyperspectral imaging DNA sequencer called "ASTRAL" has been under development (O'Brien et al 1998). This system is distinguished from other plate/gel-based systems by the hyperspectral detection system, which can image the entire read area of the gel at once, eliminating the need for a scanning detector; thus, the sequencer has no moving parts.…”

Section: Slab-based Sequencersmentioning

confidence: 99%

Automation for Genomics, Part Two: Sequencers, Microarrays, and Future Trends

Meldrum¹

2000

Genome Res.

116

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Automation for genomics has enabled a 43-fold increase in the total finished human genomic sequence in the world in the past four years. This is the second half of a two-part, noncomprehensive review that presents an overview of different types of automation equipment used in genome sequencing. The first part of the review, published in the previous issue, focused on automated procedures used to prepare DNA for sequencing or analysis. This second part of the review presents a look at available DNA sequencers and array technology and concludes with a look at future technologies. Alternate sequencing technologies including mass spectrometry, biochips, and single molecule analysis are included in this review.Sequencing data, essential for many of the medical breakthroughs of today and tomorrow, is currently accumulating at an exponential rate, largely because of advances in sequencing methods and laboratory automation. Instruments are available that can automate nearly every step in the large-scale sequencing process.The first article in this two-part review (Meldrum 2000) presented a description of automation designed for isolating DNA, cloning or amplifying DNA, preparing enzymatic sequencing reactions, and purifying DNA. Part one of the review also showed that the majority of genome centers use both in-house automation and commercial automation (Collins et al. 1998;Wendl et al. 1998;Mullikin and McMurragy 1999;Spurr et al. 1999; see http://www.genome.org for a supplementary table providing a snapshot view of automation used at a number of different genome centers).In this, the second part of the review, the focus is on automation after DNA preparation and sequencing reactions-on obtaining the sequence and its analysis. A discussion of future technologies for DNA sequencing projects is also included. (See the web sites referenced for photos or further details on the instruments presented.)

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ASTRAL, a hyperspectral imaging DNA sequencer

Cited by 12 publications

References 5 publications

Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays

Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays

Histometrics: Improvement of the dynamic range of fluorescently stained proteins resolved in electrophoretic gels using hyperspectral imaging

Automation for Genomics, Part Two: Sequencers, Microarrays, and Future Trends

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