Matt:LabNotes/2014-9-6: Difference between revisions

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**[[Hosuk:LabNotes/2014-8-22 | 20X objective Full decoding]] and [[Hosuk:LabNotes/2014-8-29 | 63X objective Full decoding]]
**[[Hosuk:LabNotes/2014-8-22 | 20X objective Full decoding]] and [[Hosuk:LabNotes/2014-8-29 | 63X objective Full decoding]]


*Add 100ul Padlock Probe Mix
*Add 100ul Padlock Probe Mix (14pM per padlock probe)
{| {{table}}
{| {{table}}
| align="center" style="background:#f0f0f0;"|'''Components'''
| align="center" style="background:#f0f0f0;"|'''Components'''
Line 28: Line 28:
*Add 200ul 0.5 uM FISSEQ_ppRCA primer in 2X SSC (pre-heated to 75C) and incubate at 45C for 30min
*Add 200ul 0.5 uM FISSEQ_ppRCA primer in 2X SSC (pre-heated to 75C) and incubate at 45C for 30min
*Wash with 2X SSC twice
*Wash with 2X SSC twice
*Add 200ul RCA Mix and incubate at 30C for overnight
*Add 200ul RCA Mix and incubate at 30C for 16hrs
**H2O : 172uL
**H2O : 172uL
**10x Buffer : 20uL
**10x Buffer : 20uL
Line 34: Line 34:
**a-dUTP : 4uL
**a-dUTP : 4uL
**Phi29 (100 U/µl) : 2uL
**Phi29 (100 U/µl) : 2uL
*Wash with 1X PBS once
*Add 200ul BS(PEG)9 (4ul BS(PEG)9 + 196ul 1X PBS) at RT for 1hr
*Wash with 1X PBS twice
*Add 200ul 1M Tris pH 8.0 at RT for 30min
*Wash with 1X PBS twice and store in PBS
===7 Cycle 3-Dye Decoding===
*Saved in 9-9-2014
*Used 0.5uM of each dcProbe
**For each cycle, 3 dcProbes were added together
===Image Analysis===
*Hosuk aligned and cropped images
**One fluorescent image is selected from each cycle to use for aligning and stored in directory: '0.Align'
**All the aligned images stored in directory: '1.Shift'
**All the aligned and cropped images stored in directory: '1.MIP'
*Made MIP of aligned and cropped images
**Filename prefix: MIP_Crop_MIP_
**Stored in directory: '2.AllMIP'
*Run PISA on MIP_Crop_MIP_
**Chose settings to maximize number of features
***Gaussian std: 2
***upper bound: -2e-4
***area upper: 200
***area lower: 8
***axratio lower: .6
***circ upper: 1.6
***circ lower: .8
***perim conn: 8
***bkgmult lower: 3
**Save BW file with prefix: BW_MIP_Crop_MIP_
*Run MasterRun.m in Matlab_FullDecode_PISAMask/FISSEQ_Decoding
**Put together by Hosuk
**immultiply BW mask with each aligned and cropped image and stores output in '3.Masked'
**For every 'feature location' in each of the 21 images, calculates average intensity and compares to the other dyes
**Use a normalized threshold of 0.2 to determine 'No Signal' during that cycle
===Results===
====Position 1====
*3,325 total features counted in PISA (237.5/cell)
*221 features had barcodes that mapped to rolonies (15.8/cell)
*Mapped barcodes represent 37 different genes
{| {{table}}
| align="center" style="background:#f0f0f0;"|'''Gene'''
| align="center" style="background:#f0f0f0;"|'''Barcode'''
| align="center" style="background:#f0f0f0;"|'''Num'''
|-
| COL1A1||3013130||113
|-
| GREM1||332120||34
|-
| GEMIN2||3020131||17
|-
| HAT1||3020212||9
|-
| IGSF10||1013130||7
|-
| RTN4||2033012||4
|-
| VAX2||2320102||3
|-
| RUNX1||3021203||2
|-
| GSK3B||3020211||2
|-
| TMEM176A||2232200||2
|-
| HNF1A||3020231||2
|-
| SOHLH2||2130203||1
|-
| CLCN7||132110||1
|-
| AHNAK||21312||1
|-
| RPLP0P2||2032120||1
|-
| SERPINA12||2101202||1
|-
| NUMB||3021101||1
|-
| PLEK2||3021130||1
|-
| IL1RN||1020131||1
|-
| HOXD1||1010131||1
|-
| RUNX2||3021210||1
|-
| ABHD12B||11233||1
|-
| EPHB4||232012||1
|-
| SLC17A5||2110201||1
|-
| ERBB3||3013330||1
|-
| PLEKHA4||2010211||1
|-
| ITGB4||3020331||1
|-
| FOXA1||3020112||1
|-
| HTR1B||1011130||1
|-
| CDH1||3013110||1
|-
| PSG11||2020131||1
|-
| KPRP||1033130||1
|-
| FAM196A||301222||1
|-
| FAM38A||301322||1
|-
| ZNF649||3001222||1
|-
| CCDC88C||120222||1
|-
| CDR1||130113||1
|}
*PISA segmentation to identify features (red) from raw image (blue)
[[File:MIP_Crop_MIP_PGP1F_S2_2ndRol20140414_Decode3rd_PISA7Overlay_3325.png | 650px]]
*Tetraplot of average intensities does not look good
[[File:20140909_Tetraplot_Pos1.PNG | 650px]]
*Red: Cy5
*Green: Cy3
*Blue: Alexa-488
====Position 2====
*3,610 total features counted in PISA (278/cell)
*342 features had barcodes that mapped to rolonies (26.3/cell)
*Mapped barcodes represent 49 different genes
{| {{table}}
| align="center" style="background:#f0f0f0;"|'''Gene'''
| align="center" style="background:#f0f0f0;"|'''Barcode'''
| align="center" style="background:#f0f0f0;"|'''Num'''
|-
| COL1A1||3013130||172
|-
| GEMIN2||3020131||29
|-
| HAT1||3020212||23
|-
| RUNX1||3021203||13
|-
| COL1A2||3013201||12
|-
| IL22||1020212||6
|-
| NUMB||3021101||5
|-
| ANXA5||31122||5
|-
| ERBB3||3013330||4
|-
| CLDN20||132210||3
|-
| PROX1||2013120||3
|-
| GRB7||3020133||3
|-
| CDH1||3013110||3
|-
| GSK3B||3020211||3
|-
| GREM1||332120||3
|-
| CTNNA1||3013210||2
|-
| APOB48R||31222||2
|-
| IL1RN||1020131||2
|-
| PRSS35||2013320||2
|-
| CHAT||3013120||2
|-
| RUNX2||3021210||2
|-
| TAT||2211100||2
|-
| POU1F1||2011130||1
|-
| KAT2B||3020332||1
|-
| PRSS38||2013330||1
|-
| ITGB4||3020331||1
|-
| GAP43||320332||1
|-
| PLEK2||3021130||1
|-
| AHNAK||21312||1
|-
| IGSF10||1013130||1
|-
| RASAL3||2022320||1
|-
| PRG2||2012320||1
|-
| CCDC62||120122||1
|-
| DDX4||3013230||1
|-
| IL1B||1020121||1
|-
| IL29||1020233||1
|-
| GFAP||3020132||1
|-
| IGF2BP2||3020233||1
|-
| STX1B||2202102||1
|-
| RPRM||2032130||1
|-
| FGFR1||3020111||1
|-
| PROX2||2013130||1
|-
| CCDC144NL||113202||1
|-
| POU4F3||2011230||1
|-
| KCNE2||1023130||1
|-
| EGF||3013310||1
|-
| TGFB1||2221300||1
|-
| LGALS1||1103023||1
|-
| OMD||1231100||1
|}
*PISA segmentation to identify features (red) from raw image (blue)
[[File:MIP_Crop_MIP_PGP1F_S2_2ndRol20140414_Decode3rd_PISA7Overlay_3610.png | 650px]]
*Tetraplot of average intensities does not look good
[[File:20140909_Tetraplot_Pos2.PNG | 650px]]
*Red: Cy5
*Green: Cy3
*Blue: Alexa-488
===Conclusion===
*No improvement over [[Hosuk:LabNotes/2014-8-29 | 63X objective Full decoding of 2nd rolonies]]
**Didn't look at identical position but since results were slightly worse for both positions here, suggests this method is not effective

Latest revision as of 02:44, 28 September 2014

"Tertiary" Rolony Synthesis[edit]

  • Hypothesis: We can increase the number of gene-specific rolonies (previously known as secondary rolonies) by repeating padlock probe capture and rolonization on samples with primary and secondary rolonies
    • There should be more targets for the padlock probes to capture since all primary rolonies are still fixed and 100% of secondary rolonies will be possible targets for padlock probes
    • May increase amplification bias

Procedure[edit]

  • Add 100ul Padlock Probe Mix (14pM per padlock probe)
Components Volume
5 U/ul Ampligase 10
10X Ampligase Buffer 10
908 nM Agi26k0gap 18.6
H2O 61.4
Total 100
  • Incubate at 37C for 30min
  • Incubate at 45C for 26hrs
  • Wash with 1X PBS twice
  • Add 200ul 0.5 uM FISSEQ_ppRCA primer in 2X SSC (pre-heated to 75C) and incubate at 45C for 30min
  • Wash with 2X SSC twice
  • Add 200ul RCA Mix and incubate at 30C for 16hrs
    • H2O : 172uL
    • 10x Buffer : 20uL
    • dNTP : 2uL
    • a-dUTP : 4uL
    • Phi29 (100 U/µl) : 2uL
  • Wash with 1X PBS once
  • Add 200ul BS(PEG)9 (4ul BS(PEG)9 + 196ul 1X PBS) at RT for 1hr
  • Wash with 1X PBS twice
  • Add 200ul 1M Tris pH 8.0 at RT for 30min
  • Wash with 1X PBS twice and store in PBS

7 Cycle 3-Dye Decoding[edit]

  • Saved in 9-9-2014
  • Used 0.5uM of each dcProbe
    • For each cycle, 3 dcProbes were added together

Image Analysis[edit]

  • Hosuk aligned and cropped images
    • One fluorescent image is selected from each cycle to use for aligning and stored in directory: '0.Align'
    • All the aligned images stored in directory: '1.Shift'
    • All the aligned and cropped images stored in directory: '1.MIP'
  • Made MIP of aligned and cropped images
    • Filename prefix: MIP_Crop_MIP_
    • Stored in directory: '2.AllMIP'
  • Run PISA on MIP_Crop_MIP_
    • Chose settings to maximize number of features
      • Gaussian std: 2
      • upper bound: -2e-4
      • area upper: 200
      • area lower: 8
      • axratio lower: .6
      • circ upper: 1.6
      • circ lower: .8
      • perim conn: 8
      • bkgmult lower: 3
    • Save BW file with prefix: BW_MIP_Crop_MIP_
  • Run MasterRun.m in Matlab_FullDecode_PISAMask/FISSEQ_Decoding
    • Put together by Hosuk
    • immultiply BW mask with each aligned and cropped image and stores output in '3.Masked'
    • For every 'feature location' in each of the 21 images, calculates average intensity and compares to the other dyes
    • Use a normalized threshold of 0.2 to determine 'No Signal' during that cycle

Results[edit]

Position 1[edit]

  • 3,325 total features counted in PISA (237.5/cell)
  • 221 features had barcodes that mapped to rolonies (15.8/cell)
  • Mapped barcodes represent 37 different genes
Gene Barcode Num
COL1A1 3013130 113
GREM1 332120 34
GEMIN2 3020131 17
HAT1 3020212 9
IGSF10 1013130 7
RTN4 2033012 4
VAX2 2320102 3
RUNX1 3021203 2
GSK3B 3020211 2
TMEM176A 2232200 2
HNF1A 3020231 2
SOHLH2 2130203 1
CLCN7 132110 1
AHNAK 21312 1
RPLP0P2 2032120 1
SERPINA12 2101202 1
NUMB 3021101 1
PLEK2 3021130 1
IL1RN 1020131 1
HOXD1 1010131 1
RUNX2 3021210 1
ABHD12B 11233 1
EPHB4 232012 1
SLC17A5 2110201 1
ERBB3 3013330 1
PLEKHA4 2010211 1
ITGB4 3020331 1
FOXA1 3020112 1
HTR1B 1011130 1
CDH1 3013110 1
PSG11 2020131 1
KPRP 1033130 1
FAM196A 301222 1
FAM38A 301322 1
ZNF649 3001222 1
CCDC88C 120222 1
CDR1 130113 1
  • PISA segmentation to identify features (red) from raw image (blue)

File:MIP Crop MIP PGP1F S2 2ndRol20140414 Decode3rd PISA7Overlay 3325.png

  • Tetraplot of average intensities does not look good

File:20140909 Tetraplot Pos1.PNG

  • Red: Cy5
  • Green: Cy3
  • Blue: Alexa-488

Position 2[edit]

  • 3,610 total features counted in PISA (278/cell)
  • 342 features had barcodes that mapped to rolonies (26.3/cell)
  • Mapped barcodes represent 49 different genes
Gene Barcode Num
COL1A1 3013130 172
GEMIN2 3020131 29
HAT1 3020212 23
RUNX1 3021203 13
COL1A2 3013201 12
IL22 1020212 6
NUMB 3021101 5
ANXA5 31122 5
ERBB3 3013330 4
CLDN20 132210 3
PROX1 2013120 3
GRB7 3020133 3
CDH1 3013110 3
GSK3B 3020211 3
GREM1 332120 3
CTNNA1 3013210 2
APOB48R 31222 2
IL1RN 1020131 2
PRSS35 2013320 2
CHAT 3013120 2
RUNX2 3021210 2
TAT 2211100 2
POU1F1 2011130 1
KAT2B 3020332 1
PRSS38 2013330 1
ITGB4 3020331 1
GAP43 320332 1
PLEK2 3021130 1
AHNAK 21312 1
IGSF10 1013130 1
RASAL3 2022320 1
PRG2 2012320 1
CCDC62 120122 1
DDX4 3013230 1
IL1B 1020121 1
IL29 1020233 1
GFAP 3020132 1
IGF2BP2 3020233 1
STX1B 2202102 1
RPRM 2032130 1
FGFR1 3020111 1
PROX2 2013130 1
CCDC144NL 113202 1
POU4F3 2011230 1
KCNE2 1023130 1
EGF 3013310 1
TGFB1 2221300 1
LGALS1 1103023 1
OMD 1231100 1
  • PISA segmentation to identify features (red) from raw image (blue)

File:MIP Crop MIP PGP1F S2 2ndRol20140414 Decode3rd PISA7Overlay 3610.png

  • Tetraplot of average intensities does not look good

File:20140909 Tetraplot Pos2.PNG

  • Red: Cy5
  • Green: Cy3
  • Blue: Alexa-488

Conclusion[edit]