Matt:LabNotes/2015-11-6

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Revision as of 04:13, 12 November 2015 by >Mzcai (Created page with "=Analyzing RNAscope Images= *Genes **PDE1A (expect in layers 2,4, and 5) **SLC17A7 (expect everywhere but not uniform) **OLFM1 **RELN (expect in layer 1) *Three samples **PDE...")
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Analyzing RNAscope Images

  • Genes
    • PDE1A (expect in layers 2,4, and 5)
    • SLC17A7 (expect everywhere but not uniform)
    • OLFM1
    • RELN (expect in layer 1)
  • Three samples
    • PDE1A + OLFM1 + SLC17A7
    • PDE1A + OLFM1
    • RELN + SLC17A7

Strategy

    • Images are RGB so need to convert to grayscale
      • Green channel can just use green RGB values
      • Magenta channel has equal Red and Blue RGB values at every pixel so just use blue RGB values
      • Yellow channel has a lot of red background and even though green RGB values are lower at spots it is high enough; use green RGB values
    • Divide each tile (which is approx 5,600 x 49,660 pixels) into 48 2048x2048 sub-images
      • With long edge aligned vertically, cut horizontally into 24 sub-images every 2048 pixels
      • Cut vertically the middle 4096 pixels into two 2048 strips (indices 786-2833 and 2834-4881)
    • Count number of nuclei and spots for each gene in each sub-image
    • Expect to see some pattern or trend in vertical direction
    • Left and right sub-images are like replicate samples and should be similar

Counting transcript dots

FindRNAscopeSpots.m

  • Apply gaussian filter
    • Tried sigma = 0.5, 0.75, 0.8, and 1 and found 0.75 works best
  • Apply threshold
    • Tried 10 and 20 and found 20 to be best (avoids false positives from next step)
  • Find local maximum (peaks)
  • Count with bwconncomp

Validate

  • c3_3-1 cropped (increased and brightness and contrast) -> marked local max after filtering

File:C3 3-1 cropped.jpg -> File:C3 3-1 cropped gaussfilt75.jpg

  • c4_1-2 cropped -> marked local maximum after filtering

File:C4 1-2 cropped.jpg -> File:C4 1-2 cropped gaussfilt75.jpg

Counting nuclei

NucC.m

  • Erode and dilate to remove noise within nuclei
  • Convert to binary image
  • Segment with watershed transformation
  • NucC.m counts every "little" nuclei while another script: count_nuclei_63x.m counts only large "typical" nuclei
    • Using NucC since I think it more accurately represents number of cells in the image

Results

  • Divided transcript counts by number of nuclei in each 2048x2048 image
  • Calculated average and standard error of the two replicates (left and right images)
  • Plotted average and used error bars to represent SEM

RELN+SLC17A7

  • Tile 1
    • RELN has expected spike
    • Actually only a couple cells had all the RELN expression, not uniform in that image layer

File:R+S Tile 1 Spots Per Nuclei.JPG

  • Tile 2

PDE1A+OLFM1

  • Tile 1
  • Tile 2

PDE1A+OLFM1+SLC17A7

  • Tile 1
  • Tile 2