High-throughput screen to identify and optimize NOT gate receptors for cell therapy
Data files
Jul 18, 2024 version files 8.92 MB
-
Fig_2C-CEA_CAR_MSLN.zip
630.05 KB
-
Fig_2C-EGFR_CAR_HLA02.zip
1.75 MB
-
Fig_2C-EGFR_CAR_NYESO.zip
5.14 MB
-
Fig_3.Jurkat_EGFR_STRONG_activation_round.zip
388.70 KB
-
Fig_3.Jurkat_EGFR_STRONG_Blocking_round1.zip
540.14 KB
-
Fig_3.Jurkat_EGFR_STRONG_Blocking_round2.zip
457.01 KB
-
README.md
5.15 KB
Abstract
Logic-gated engineered cells are an emerging therapeutic modality that can take advantage of molecular profiles to focus medical interventions on specific tissues in the body. However, the increased complexity of these engineered systems may pose a challenge for prediction and optimization of their behavior. Here we describe the design and testing of a flow-cytometry-based screening system to rapidly select functional inhibitory receptors from a pooled library of candidate constructs. In proof-of-concept experiments, this approach identifies inhibitory receptors that can operate as NOT gates when paired with activating receptors. The method may be used to generate large datasets to train machine-learning models to better predict and optimize the function of logic-gated cell therapeutics.
[https://doi.org/10.5061/dryad.np5hqc02v]
Here we designed a flow-cytometry-based screening system to rapidly select functional inhibitory receptors from a pooled library of candidate constructs. This screen incorporates an NFAT-responsive reporter cell line to allow selection of reporter (+) and (-) cell populations by FACS after exposure to different stimuli. A library of blocker variants was constructed to test the concept of Tmod optimization and structure-activity inference from this cell-based functional screen. A Jurkat reporter clone was identified that maintained low background expression of green fluorescent protein (GFP) in the absence of a stimulus, and strong GFP induction by antigen. This Jurkat screening system for Tmod blockers was robust across multiple constructs, with signal:noise properties suggesting that enrichment of up to 15-fold was possible. Proof-of-concept experiments using a library of blocker variants demonstrated enrichment of active blockers.
Description of the data and file structure
Each folder correspond to the Figures indicated. Generally there is indication of the antigen to which the cells were exposed. Gating strategy: FSC vs SSC to gate for cells, when present DAPI positive correspond to live cells.
Abbreviation used:
A = activator (EGFR or CEA), AB = activator and blocker (EGFR or CEA + HLA/NYESO or MSLN as indicated), A-B- = activator and blocker negative
WT= Wild Type, JKT=Jurkat, CTL= Control, Pre-sort= Cells before round of sorting, ON= overnight
Description of each figure
Fig 2C-EGFR CAR HLA02
Data are used in Figure 2C; CAR= EGFR, Blocker= HLA-A*02
Validation of Jurkat reporter cell line. HeLa cells that express A antigen (EGFR) were presented to the Jurkat reporter cell lines (transduced with EGFR CAR), to activate the NFAT promoter and GFP signal. Maximal activator enrichment was calculated from the flow data as %GFP(+) in the activated population to the right of the gate divided by %GFP(+) of the uninduced population to the right of the gate. Next, Jurkat cells that expressed both the EGFR CAR and an HLA-A*02 blocker were cocultured with HeLa cells that express both target antigens (A(+)B(+)). Maximal blocker enrichment was calculated as %GFP(-) in the blocked population divided by %GFP(-) in the unblocked population.
Fig 2C-CEA CAR MSLN
Data are used in Figure 2C; CAR= CEA, Blocker= MSLN
Validation of Jurkat reporter cell line. HeLa cells that express A antigen (CEA) were presented to the Jurkat reporter cell lines (transduced with CEA CAR), to activate the NFAT promoter and GFP signal. Maximal activator enrichment was calculated from the flow data as %GFP(+) in the activated population to the right of the gate divided by %GFP(+) of the uninduced population to the right of the gate. Next, Jurkat cells that expressed both the CEA CAR and an MSLN blocker were cocultured with HeLa cells that express both target antigens (A(+)B(+)). Maximal blocker enrichment was calculated as %GFP(-) in the blocked population divided by %GFP(-) in the unblocked population.
Fig 2C-EGFR CAR NYESO
Data are used in Figure 2C; CAR= EGFR, Blocker= NYESO
Validation of Jurkat reporter cell line. HeLa cells that express A antigen (EGFR) were presented to the Jurkat reporter cell lines (transduced with EGFR CAR), to activate the NFAT promoter and GFP signal. Maximal activator enrichment was calculated from the flow data as %GFP(+) in the activated population to the right of the gate divided by %GFP(+) of the uninduced population to the right of the gate. Next, Jurkat cells that expressed both the EGFR CAR and an NYESO blocker were cocultured with HeLa cells that express both target antigens (A(+)B(+)). Maximal blocker enrichment was calculated as %GFP(-) in the blocked population divided by %GFP(-) in the unblocked population.
Fig 3.Jurkat_EGFR_STRONG_activation round
Data are used in Figure 3. GFP signal of pre-sort and post-sorting (GFP positive (activated cells))
Jurkat reporter cells that express the strong EGFR CAR and blocker library were cocultured with A(+) HeLa cells that express EGFR, sorted for the GFP(+) population to enrich for cells able to activate.
Fig 3.Jurkat_EGFR_STRONG_Blocking round1
Data are used in Figure 3. GFP signal of pre-sort and post-sorting for blocking round 1(GFP positive (activated cells) and GFP negative (blocked cells))
Jurkat reporter activated cells were cocultured with A(+)B(+) HeLa cells that express EGFR and NY-ESO-1 trimer and sorted for GFP(+) and GFP(-) fractions.
Fig 3.Jurkat_EGFR_STRONG_Blocking round2
Data are used in Figure 3. GFP signal of pre-sort and post-sorting for blocking round 2(GFP positive (activated cells) and GFP negative (blocked cells))
Jurkat reporter sorted for GFP - fraction in the Blocking round 1, were cocultured with A(+)B(+) HeLa cells that express EGFR and NY-ESO-1 trimer and sorted for GFP(+) and GFP(-) fractions.