Matt:LabNotes/2015-11-6: Difference between revisions
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>Mzcai m (→PDE1A+OLFM1) |
>Mzcai |
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*Genes | *Genes | ||
**PDE1A (expect in layers 2,4, and 5) | **PDE1A (expect in layers 2,4, and 5) | ||
[[File:ABI_ISH_PDE1A.JPG|350px]] | |||
**SLC17A7 (expect everywhere but not uniform) | **SLC17A7 (expect everywhere but not uniform) | ||
[[File:ABI_ISH_SLC17A7.JPG|350px]] | |||
**OLFM1 | **OLFM1 | ||
**RELN (expect in layer 1) | **RELN (expect in layer 1) | ||
[[File:ABI_ISH_RELN.JPG|350px]] | |||
*Three samples | *Three samples | ||
Line 55: | Line 58: | ||
**RELN has expected spike | **RELN has expected spike | ||
**Actually only a couple cells had all the RELN expression, not uniform in that image layer | **Actually only a couple cells had all the RELN expression, not uniform in that image layer | ||
[[File:R%2BS_Tile_1_Spots_Per_Nuclei.JPG | | [[File:R%2BS_Tile_1_Spots_Per_Nuclei.JPG | 350px]] | ||
*Tile 2 | *Tile 2 | ||
**Y-slices 20-24 had very few or no cells because tissue ended there so removed them | **Y-slices 20-24 had very few or no cells because tissue ended there so removed them | ||
[[File:R%2BS_Tile_2_Spots_Per_Nuclei.JPG | | [[File:R%2BS_Tile_2_Spots_Per_Nuclei.JPG | 350px]] [[File:R%2BS_Tile_2_Spots_Per_Nuclei_RELNzoomed.JPG | 350px]] | ||
[[File:R%2BS_Tile_2_Spots_Per_Nuclei_RELNzoomed.JPG | | |||
===PDE1A+OLFM1=== | ===PDE1A+OLFM1=== | ||
Line 67: | Line 69: | ||
*Tile 1 | *Tile 1 | ||
[[File:P%2BO_Tile_2_Spots_Per_Nuclei.JPG| | [[File:P%2BO_Tile_2_Spots_Per_Nuclei.JPG|350px]] [[File:P%2BO_Tile_1_Spots_Per_Nuclei_PDE1Azoomed.JPG|350px]] | ||
[[File:P%2BO_Tile_1_Spots_Per_Nuclei_PDE1Azoomed.JPG| | |||
*Tile 2 | *Tile 2 | ||
[[File:P%2BO_Tile_2_Spots_Per_Nuclei.JPG| | [[File:P%2BO_Tile_2_Spots_Per_Nuclei.JPG|350px]] [[File:P%2BO_Tile_2_Spots_Per_Nuclei_PDE1Azoomed.JPG|350px]] | ||
[[File:P%2BO_Tile_2_Spots_Per_Nuclei_PDE1Azoomed.JPG| | |||
===PDE1A+OLFM1+SLC17A7=== | ===PDE1A+OLFM1+SLC17A7=== |
Revision as of 01:33, 13 November 2015
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
- Images are RGB so need to convert to grayscale
Counting transcript dots
- 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
- 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
- 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
- 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%
- Tile 1
File:P+O Tile 2 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
- Tile 1
- Tile 2