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Computational analysis of Me31B colocalization with P-body proteins in somatic and synaptic foci . (A) Stages of image processing during automated detection of immunostained puncta (see ). The original image (i) is transformed into a mesh-plot (ii). The local background (iii) is subtracted from this image to yield image (iv). Each object in this background-subtracted image is fit to Gaussian parameters using an SMEM (Split-Merge Estimation-Maximization) technique and the result (v) is used as a mask to analyze underlying spots in the original image (vi). (B–D) Quantified colocalization of Dcp1, Pcm, and Staufen with Me31B. The graphs plot cumulative fraction of Dcp1, Pcm, or Stau positive particles found within a specific distance from the closest Me31B containing particle (fraction on the “ y ” axis is plotted versus distance on the “ x ”). Blue curves correspond to somatic puncta and red curves to synaptic ones (for each curve 20 images and about 5000 spots-per-channel, were analyzed). Distances are as measured by <t>Spotnik</t> from the computationally determined centroids for each spot, as described by the Gaussian modeling technique. However the actual position of these centroids are naturally limited by the resolution of light microscopy. Thus, spots separated by less than about 0.30 μ should be assumed to colocalize exactly. (B) Cumulative plot showing the fraction of Dcp1 foci ( y ) that are within ( x ) micrometers from an Me31B foci described using Gaussian modeling. The quick rise of the blue curve (somatic foci) compared to the red curve (synaptic foci) indicates substantially tighter colocalization of Me31B with P-body markers in somatic particles compared to synaptic ones. (C,D) Plots similar to (B) for Pcm and Staufen.
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Computational analysis of Me31B colocalization with P-body proteins in somatic and synaptic foci . (A) Stages of image processing during automated detection of immunostained puncta (see ). The original image (i) is transformed into a mesh-plot (ii). The local background (iii) is subtracted from this image to yield image (iv). Each object in this background-subtracted image is fit to Gaussian parameters using an SMEM (Split-Merge Estimation-Maximization) technique and the result (v) is used as a mask to analyze underlying spots in the original image (vi). (B–D) Quantified colocalization of Dcp1, Pcm, and Staufen with Me31B. The graphs plot cumulative fraction of Dcp1, Pcm, or Stau positive particles found within a specific distance from the closest Me31B containing particle (fraction on the “ y ” axis is plotted versus distance on the “ x ”). Blue curves correspond to somatic puncta and red curves to synaptic ones (for each curve 20 images and about 5000 spots-per-channel, were analyzed). Distances are as measured by Spotnik from the computationally determined centroids for each spot, as described by the Gaussian modeling technique. However the actual position of these centroids are naturally limited by the resolution of light microscopy. Thus, spots separated by less than about 0.30 μ should be assumed to colocalize exactly. (B) Cumulative plot showing the fraction of Dcp1 foci ( y ) that are within ( x ) micrometers from an Me31B foci described using Gaussian modeling. The quick rise of the blue curve (somatic foci) compared to the red curve (synaptic foci) indicates substantially tighter colocalization of Me31B with P-body markers in somatic particles compared to synaptic ones. (C,D) Plots similar to (B) for Pcm and Staufen.

Journal: Frontiers in Neural Circuits

Article Title: The Me31B DEAD-Box Helicase Localizes to Postsynaptic Foci and Regulates Expression of a CaMKII Reporter mRNA in Dendrites of Drosophila Olfactory Projection Neurons

doi: 10.3389/fncir.2010.00121

Figure Lengend Snippet: Computational analysis of Me31B colocalization with P-body proteins in somatic and synaptic foci . (A) Stages of image processing during automated detection of immunostained puncta (see ). The original image (i) is transformed into a mesh-plot (ii). The local background (iii) is subtracted from this image to yield image (iv). Each object in this background-subtracted image is fit to Gaussian parameters using an SMEM (Split-Merge Estimation-Maximization) technique and the result (v) is used as a mask to analyze underlying spots in the original image (vi). (B–D) Quantified colocalization of Dcp1, Pcm, and Staufen with Me31B. The graphs plot cumulative fraction of Dcp1, Pcm, or Stau positive particles found within a specific distance from the closest Me31B containing particle (fraction on the “ y ” axis is plotted versus distance on the “ x ”). Blue curves correspond to somatic puncta and red curves to synaptic ones (for each curve 20 images and about 5000 spots-per-channel, were analyzed). Distances are as measured by Spotnik from the computationally determined centroids for each spot, as described by the Gaussian modeling technique. However the actual position of these centroids are naturally limited by the resolution of light microscopy. Thus, spots separated by less than about 0.30 μ should be assumed to colocalize exactly. (B) Cumulative plot showing the fraction of Dcp1 foci ( y ) that are within ( x ) micrometers from an Me31B foci described using Gaussian modeling. The quick rise of the blue curve (somatic foci) compared to the red curve (synaptic foci) indicates substantially tighter colocalization of Me31B with P-body markers in somatic particles compared to synaptic ones. (C,D) Plots similar to (B) for Pcm and Staufen.

Article Snippet: We created “Spotnik” a MatLab plug-in which allowed us to automate the quantitative analysis of spot colocalization across red and green channels of a double-stained image.

Techniques: Transformation Assay, Light Microscopy