Matt:LabNotes/2015-11-6

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Analyzing RNAscope Images[edit]

  • Genes
    • PDE1A (expect in layers 2,4, and 5)

File:ABI ISH PDE1A.JPG

    • SLC17A7 (expect everywhere but not uniform)

File:ABI ISH SLC17A7.JPG

    • OLFM1
    • RELN (expect in layer 1)

File:ABI ISH RELN.JPG

  • Three samples
    • PDE1A + OLFM1 + SLC17A7
    • PDE1A + OLFM1
    • RELN + SLC17A7

Strategy[edit]

    • 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[edit]

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[edit]

  • 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[edit]

NucC.m

  • nucC(Image,40)
  • Erode and dilate to remove noise within nuclei
    • Strel size = 40
  • 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[edit]

  • 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[edit]

  • 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
    • Y-slices 20-24 had very few or no cells because tissue ended there so removed them

File:R+S Tile 2 Spots Per Nuclei.JPG File:R+S Tile 2 Spots Per Nuclei RELNzoomed.JPG

PDE1A+OLFM1[edit]

  • Yslice 24 had no nuclei
    • Yslices 20-24 had very little nuclei and nuclei counting algorithm tended to overestimate for these images, sometimes by >100%
  • I notice similarities in spikes at Y-Slice 5, 10, 15, and 18-19
  • Tile 1

File:P+O Tile 1 Spots Per Nuclei.JPG File:P+O Tile 1 Spots Per Nuclei PDE1Azoomed.JPG

  • Tile 2

File:P+O Tile 2 Spots Per Nuclei.JPG File:P+O Tile 2 Spots Per Nuclei PDE1Azoomed.JPG

PDE1A+OLFM1+SLC17A7[edit]

  • Tile 1

File:P+S+O Tile 1 Spots Per Nuclei.JPG File:P+S+O Tile 1 Spots Per Nuclei zoomedPDE1A.JPG

  • Tile 2

File:P+S+O Tile 2 Spots Per Nuclei.JPG File:P+S+O Tile 2 Spots Per Nuclei zoomedPDE1A.JPG