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Data Summary 2019 #40
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also cc @DavidStirling |
Hi Greg,
This is great! Thanks so much for putting this together. I’ll review the notes on our end and add any relevant information (likely Monday). I’m also putting together a master spreadsheet of all the analyses to standardize the format of data we send your way and make it easier to analyze (it’ll include drug treatments, cell plating density, etc. in a clear way).
…-Megan
From: Greg Way<mailto:[email protected]>
Sent: Thursday, December 19, 2019 12:15
To: broadinstitute/profiling-resistance-mechanisms<mailto:[email protected]>
Cc: MEK<mailto:[email protected]>; Mention<mailto:[email protected]>
Subject: [broadinstitute/profiling-resistance-mechanisms] Data Summary 2019 (#40)
We are nearing the end of 2019, and we are starting to increase data collection. This issue will document the data we have currently collected.
Overall Summary
In summary, we've made a lot of progress in optimizing data collection, and a lot of the profiling results look promising. For example, we've identified some morphology features that are consistently different between mutant and wild-type clones. We can use the current data to finalize optimal conditions and optimal plate layouts so that we can efficiently scale up in 2020.
We can decide to scale up to profile different bortezomib resistant clones in HCT116. This would answer how many resistance mechanisms does this system develop. We could also scale up to test different proteasome inhibitors. The hypothesis there is that HCT116 cells develop the same resistance mechanism against any proteasome inhibitor. We could also scale to different cell lines, although this may be difficult since we'll have to optimize conditions again. The overall goal is to be able to predict resistance in single cells based on cell morphology. More exciting progress and questions to attack in 2020! 👀
cc @bethac07<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_bethac07&d=DwMFaQ&c=JeTkUgVztGMmhKYjxsy2rfoWYibK1YmxXez1G3oNStg&r=hW5mnjcrMosmnWxBxcZJ278DYB1dkm8wth8Ih3BIIQ4&m=EeyfVrLp-nRXCW6si8sS0JTKCgDpYRszxHvdJvfxNnM&s=5CbI8_hV15fNcL6OxvK6OYIBQys_qwuxJA6Bw4GXJy4&e=> @mekelley<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_mekelley&d=DwMFaQ&c=JeTkUgVztGMmhKYjxsy2rfoWYibK1YmxXez1G3oNStg&r=hW5mnjcrMosmnWxBxcZJ278DYB1dkm8wth8Ih3BIIQ4&m=EeyfVrLp-nRXCW6si8sS0JTKCgDpYRszxHvdJvfxNnM&s=EDlK6OoEe0DvY8_HKddhU-0cG3REohqPHSaYKZBV8mU&e=> @shntnu<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_shntnu&d=DwMFaQ&c=JeTkUgVztGMmhKYjxsy2rfoWYibK1YmxXez1G3oNStg&r=hW5mnjcrMosmnWxBxcZJ278DYB1dkm8wth8Ih3BIIQ4&m=EeyfVrLp-nRXCW6si8sS0JTKCgDpYRszxHvdJvfxNnM&s=LGNQKQM9RHGcfSdcQ_hrsIVDvdxRAzbmmztlERjcdnA&e=> @AnneCarpenter<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_AnneCarpenter&d=DwMFaQ&c=JeTkUgVztGMmhKYjxsy2rfoWYibK1YmxXez1G3oNStg&r=hW5mnjcrMosmnWxBxcZJ278DYB1dkm8wth8Ih3BIIQ4&m=EeyfVrLp-nRXCW6si8sS0JTKCgDpYRszxHvdJvfxNnM&s=zMEC1s73pGgqMUU1JeeM0AAA3jWuJtRiQ0cipOR23tc&e=>
At a Glance
HCT116 Cell Line (colorectal cancer), 2 treatments (DMSO control and Bortezomib (proteasome inhibitor)), 7 batches of data, 633 total profiles
[batch_count]<https://urldefense.proofpoint.com/v2/url?u=https-3A__user-2Dimages.githubusercontent.com_7340421_71189306-2D99b93000-2D2250-2D11ea-2D8086-2D9c5833f59cc1.png&d=DwMFaQ&c=JeTkUgVztGMmhKYjxsy2rfoWYibK1YmxXez1G3oNStg&r=hW5mnjcrMosmnWxBxcZJ278DYB1dkm8wth8Ih3BIIQ4&m=EeyfVrLp-nRXCW6si8sS0JTKCgDpYRszxHvdJvfxNnM&s=WR4dcvmM_Tgnss3mk-ubFYGQJtSRpSQqF9M0O2LzphU&e=>
We have noted a potential issue in the number of cells in each well. High confluence may lead to incorrect segmentation and inaccurate profiles. This is one issue that we are working towards solving. I note the size of the single cell profiles (.sqlite file) for each plate. The bigger the size, the higher the confluence.
Batch
Plate
sqlite file size
2019_02_15_Batch1_20X
HCT116bortezomib
11G
2019_02_15_Batch1_40X
HCT116bortezomib
4.8G
2019_03_20_Batch2
207106_exposure320
7.2G
2019_06_25_Batch3
WTClones
23G
2019_06_25_Batch3
MutClones
26G
2019_11_11_Batch4
WTmut04hWed
56G
2019_11_11_Batch4
WTmut04hTh
56G
2019_11_19_Batch5
217755
37G
2019_11_20_Batch6
217762
28G
2019_11_20_Batch6
217760
48G
2019_11_22_Batch7
217768
17G
2019_11_22_Batch7
217766
39G
Current Summary
Initial Testing
* Batch 1 - tested magnification
* We decided on 20X
* Batch 2 - Replicate of batch 1 using only 20x data
Measure different clones
* Batch 3 - Tested a bunch of different wildtype and mutation clones
Measure different time points
* Batch 4 - Tested one extra day of growth (the cells in this batch might be too confluent to use)
Measure with lower confluence
* Batch 5 - We tried to measure the same layout as batch 4 but with lower cell density
Test different confluence levels
* Batch 6 - Testing two different plating densities
* Batch 7 - Testing two different plating densities
Batch Details
Batch 1 - Acquired on 15 February 2019
Batch 1 data was acquired using two different magnifications (20X and 40X) (TODO see #28<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_broadinstitute_profiling-2Dresistance-2Dmechanisms_issues_28&d=DwMFaQ&c=JeTkUgVztGMmhKYjxsy2rfoWYibK1YmxXez1G3oNStg&r=hW5mnjcrMosmnWxBxcZJ278DYB1dkm8wth8Ih3BIIQ4&m=EeyfVrLp-nRXCW6si8sS0JTKCgDpYRszxHvdJvfxNnM&s=hHMsvsJDVnbU3eNaKXD0FmxYVLTdX0LIy8A-U6gT1Zw&e=>). The two batches are: 2019_02_15_Batch1_20X and 2019_02_15_Batch1_40X.
Notes - Batch 1
* There were three cell lines tested: CloneA, CloneE, and WT.
* The treatment tested was: Bortezomib
* There were four doses tested: 0.0, 0.7, 7.0, and 70.0`
* It is not clear if the 0.0 dose represents DMSO treatment
* Each profile was acquired in triplicate
Batch 2 - Acquired on 20 March 2019 (2019_03_20_Batch2)
Batch 2 data had the same data acquired as Batch 1. Batch 2 tested the same cell lines, the same perturbations, the same doses, and the same number of replicates as Batch 1.
Batch 3 - Acquired on 25 June 2019 (2019_06_25_Batch3)
Batch 3 data saw a shift in data collection. It is not clear if these profiles had undergone any treatment. There were two plates (MutClones and WTClones). Each plate had many different wildtype and mutant clones. There were three wildtype parental lines acquired on both plates.
Notes - Batch 3
* There were 18 mutant clones profiled: BZ001, BZ002, BZ003, ...
* There were 15 wildtype clones profiled: WT001, WT002, WT003, ...
* Wildtype parental profiles were collected on both plates
* Profiles were acquired in triplicate
Batch 4 - Acquired 11 November 2019 (2019_11_11_Batch4)
Batch 4 also saw a shift in data collection. Based on the platemap names (WTmut04hWed and WTmut04hTh) it seems like the data were acquired after 1 extra day of growth. We've also now started collecting wildtype parental clones on every plate with 9 replicates.
Notes - Batch 4
* These files are HUGE (56G) - there are too many cells here for the profiles to be reliable.
* The plates were identical
* Each profile was treated with DMSO or Bortezomib (unknown concentration)
* Four mutant clones were tested (BZ001, BZ008, BZ017, BZ018)
* Four wildtype clones were tested (WT002, WT008, WT009, WT011)
* Wildtype parental lines were acquired on both plates, with both DMSO and bortezomib treatments
* All profiles were captured in triplicate, except for wildtype parental lines treated with DMSO. These were collected 9 times.
Batch 5 - Acquired 19 November 2019 (2019_11_19_Batch5)
Batch 5 was the same experimental design as Batch 4. Batch 5 is a bit smaller than batch 4, but still very large (37G).
Notes - Batch 5
* There is only one plate measured (217755)
* Bortezomib and DMSO treatments
* The same clones and replicate numbers as Batch 4
Batch 6 - Acquired 20 November 2019 (2019_11_20_Batch6)
Batch 6 was the same experimental design as Batches 4 and 5. There were two plates acquired in Batch 6: 217760 and 217762.
Notes - Batch 6
* Bortezomib and DMSO treatments
* The same clones and replicate numbers as Batches 4 and 5
* Two different cell counts initially plated (217760 was 48G and 2177621 was 28G)
Batch 7 - Acquired 22 November 2019 (2019_11_22_Batch7)
Batch 7 was the same experimental design as Batches 4, 5, and 6. There were two plates acquired in Batch 6: 217766 and 217768.
Notes - Batch 7
* Bortezomib and DMSO treatments
* The same clones and replicate numbers as Batches 4, 5, and 6
* Two different cell counts initially plated (217766 was 39G and 217768 was 17G)
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|
@mekelley - It is also important to note that I reprocessed all replicate correlation plots in #42 (this includes updated batch 5 data, and new batch 6/7 data. I also removed I haven't yet made a formal presentation of the updates, but noting here in case you wanted to take a quick look. |
Here are some additional notes regarding the 2019 year-end summary (I'll share the master spreadsheet after the holiday break since I'm still adding to it). Batch 1:
Batch 3:
Batch 4:
Batch 5:
Batch 6:
Batch 7:
Thanks again for all of your hard work. It's been a great year, looking forward to 2020! |
Batch HeatmapsProfiles generated with pycytominer (see #50) Batch 1 (20X)Batch 1 (40X)Batch 2Batch 3Batch 4Batch 5Batch 6Batch 7 |
We are nearing the end of 2019, and we are starting to increase data collection. This issue will document the data we have currently collected.
Overall Summary
In summary, we've made a lot of progress in optimizing data collection, and a lot of the profiling results look promising. For example, we've identified some morphology features that are consistently different between mutant and wild-type clones. We can use the current data to finalize optimal conditions and optimal plate layouts so that we can efficiently scale up in 2020.
We can decide to scale up to profile different bortezomib resistant clones in HCT116. This would answer how many resistance mechanisms does this system develop. We could also scale up to test different proteasome inhibitors. The hypothesis there is that HCT116 cells develop the same resistance mechanism against any proteasome inhibitor. We could also scale to different cell lines, although this may be difficult since we'll have to optimize conditions again. The overall goal is to be able to predict resistance in single cells based on cell morphology. More exciting progress and questions to attack in 2020! 👀
cc @bethac07 @mekelley @shntnu @AnneCarpenter
At a Glance
HCT116 Cell Line (colorectal cancer), 2 treatments (DMSO control and Bortezomib (proteasome inhibitor)), 7 batches of data, 633 total profiles
We have noted a potential issue in the number of cells in each well. High confluence may lead to incorrect segmentation and inaccurate profiles. This is one issue that we are working towards solving. I note the size of the single cell profiles (
.sqlite
file) for each plate. The bigger the size, the higher the confluence.Traditionally, these files are between 10 and 25 GB in 384 well plates. These are only 96 well plates and the files are generally much larger.
Current Summary
Initial Testing
Measure different clones
Measure different time points
Measure with lower confluence
Test different confluence levels
Batch Details
Batch 1 - Acquired on 15 February 2019
Batch 1 data was acquired using two different magnifications (20X and 40X) (TODO see #28). The two batches are:
2019_02_15_Batch1_20X
and2019_02_15_Batch1_40X
.Notes - Batch 1
CloneA
,CloneE
, andWT
.Bortezomib
0.0
,0.7
,7.0
, and70.0
0.0
dose represents 0.1% DMSO (control vehicle only)Batch 2 - Acquired on 20 March 2019 (
2019_03_20_Batch2
)Batch 2 data had the same data acquired as Batch 1. Batch 2 tested the same cell lines, the same perturbations, the same doses, and the same number of replicates as Batch 1.
Batch 3 - Acquired on 25 June 2019 (
2019_06_25_Batch3
)Batch 3 data saw a shift in data collection. These cells have not undergone any treatment (not even DMSO/control vehicle). There were two plates (
MutClones
andWTClones
). Each plate had many different wildtype and mutant clones. There were three wildtype parental lines acquired on both plates.Notes - Batch 3
Batch 4 - Acquired 11 November 2019 (
2019_11_11_Batch4
)Batch 4 also saw a shift in data collection. Cells were grown for 48h before fixation on both plates, however different densities were plated (10.5x10^3 cells/well for WTmut04hWed [plate #217744] and 7x10^3 cells/well for WTmut04hTh [plate #217748]). We've also now started collecting wildtype parental clones on every plate with 9 replicates.
Notes - Batch 4
Batch 5 - Acquired 19 November 2019 (
2019_11_19_Batch5
)Batch 5 was the same experimental design as Batch 4. Batch 5 is a bit smaller than batch 4, but still very large (37G). Batch 5 was plated at 5x10^3 cells/well.
Notes - Batch 5
217755
)Batch 6 - Acquired 20 November 2019 (
2019_11_20_Batch6
)Batch 6 was the same experimental design as Batches 4 and 5. There were two plates acquired in Batch 6:
217760
and217762
. We acquired brightfield images of these plates as well.Notes - Batch 6
217760
was 48G and217762
was 28G)217760
(imaged 5 channels)217761
is the brightfield of plate217760
217762
(imaged 5 channels)217763
is the brightfield of plate217762
Batch 7 - Acquired 22 November 2019 (
2019_11_22_Batch7
)Batch 7 was the same experimental design as Batches 4, 5, and 6. There were two plates acquired in Batch 6:
217766
and217768
. Brightfield was also captured for this batch.Notes - Batch 7
217766
was 39G and217768
was 17G)217766
(imaged 5 channels)217767
is the brightfield of plate217766
217768
(imaged 5 channels)217769
is the brightfield of plate217768
edits to add important details @mekelley described in #40 (comment)
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