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Programming in situ immunofluorescence intensities through interchangeable reactions of dynamic DNA complexes.

ABSTRACT: The regulation of antibody reporting intensities is critical to various in situ fluorescence-imaging analyses. Although such control is often necessary to visualize sparse molecular targets, the ability to tune marker intensities is also essential for highly multiplexed imaging strategies in which marker reporting levels must be tuned both to optimize dynamic detection ranges and to minimize crosstalk between different signals. Existing chemical amplification approaches generally lack such control. Here, we demonstrate that linear and branched DNA complexes can be designed to function as interchangeable building blocks that can be assembled into organized, fluorescence-reporting complexes. We show that the ability to program DNA-strand-displacement reactions between these complexes offers new opportunities to deterministically tune the number of dyes that are coupled to individual antibodies in order both to increase and controllably balance marker reporting levels within fixed cells.

SUBMITTER: Zimak J

PROVIDER: S-EPMC3715551 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Programming in situ immunofluorescence intensities through interchangeable reactions of dynamic DNA complexes.

Zimak Jan J Schweller Ryan M RM Duose Dzifa Y DY Hittelman Walter N WN Diehl Michael R MR

Chembiochem : a European journal of chemical biology 20121120 18

The regulation of antibody reporting intensities is critical to various in situ fluorescence-imaging analyses. Although such control is often necessary to visualize sparse molecular targets, the ability to tune marker intensities is also essential for highly multiplexed imaging strategies in which marker reporting levels must be tuned both to optimize dynamic detection ranges and to minimize crosstalk between different signals. Existing chemical amplification approaches generally lack such contr ...[more]

PMID: 23165916

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Project description:The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster.We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data.When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging.We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm.

Dataset Information

Programming in situ immunofluorescence intensities through interchangeable reactions of dynamic DNA complexes.

Publications

Programming in situ immunofluorescence intensities through interchangeable reactions of dynamic DNA complexes.

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