KEY RESOURCES TABLE
| REAGENT or RESOURCE | SOURCE | IDENTIFIER |
|---|---|---|
|
| ||
| Chemicals, peptides, and recombinant proteins | ||
|
| ||
| lipopolysaccharide (LPS) | Sigma Aldrich, B5:055 | L2880 |
| Murine TNF | Roche | 11271156001 |
| Pam3CSK4 | InvivoGen | tlrl-pms |
| polyinosine-polycytidylic acid (Poly(I:C)) | InvivoGen | tlrl-picw |
| Synthetic CpG ODN 1668 | InvivoGen | tlrl-1668 |
| Recombinant flagellin (FLA) | InvivoGen | tlrl-flic |
| FSL1 | InvivoGen | tlrl-fsl |
| R848 | InvivoGen | tlrl-r848 |
| Recombinant Mouse-IFNβ | PBL Assay Science | 12401–1 |
| Recombinant-Murine-IFNg | PeproTech | 315–05 |
| Recombinant-Murine -IL10 | PeproTech | 210–10 |
| Recombinant-Murine-IL13 | PeproTech | 210–13 |
| Recombinant-Murine-IL4 | PeproTech | 214–14 |
| Docosahexaenoic acid | Sigma-Aldrich | D-2534 |
| TRIzol reagent | Invitrogen | 15596018 |
| Hoechst 33342 dye | Thermo Fisher | 62249 |
|
| ||
| Critical commercial assays | ||
|
| ||
| Direct-zol RNA isolation kit | Zymo Research | R2060 |
| KAPA Stranded RNA-Seq Kit | KAPA Biosystems | KK8421 |
|
| ||
| Deposited data | ||
|
| ||
| Single cell NFκB signaling dynamics | This paper | Mendeley Data: https://doi.org/10.17632/gkxzb5hcmk.1 |
| RNA-seq data of hMPDM, BMDM, and RAW 264.7 cells stimulated with LPS | This paper | GEO: GSE246566 |
|
| ||
| Experimental models: Cell lines | ||
|
| ||
| RelA-mVenus hMPs | This paper | hMPs |
| RAW 264.7 | ATCC | TIB-71 |
|
| ||
| Experimental models: Organisms/strains | ||
|
| ||
| RelAmVenus/mVenus (C57BL/6) | Adelaja et al.15 | JAX stock 38987 |
|
| ||
| Software and algorithms | ||
|
| ||
| MACKtrack - Image Analysis (single cell tracking and measurement) | Adelaja et al.15 | https://github.com/brookstaylorjr/MACKtrack |
| NFκB trajectory feature calculations | This paper | https://github.com/signalingsystemslab/polarized_macs_NFkB_response_dynamics https://doi.org/10.5281/zenodo.11099125 |
| NFκB math model and parameter inference | This paper | https://github.com/michaeliter/nfkb_param_fitting https://doi.org/10.5281/zenodo.11099470 |
| Cutadapt | Martin71 | https://github.com/marcelm/cutadapt |
| PRINSEQ | Schmieder and Edwards72 | https://sourceforge.net/projects/prinseq/files/ |
| STAR | Dobin et al.73 | https://github.com/alexdobin/STAR |
| Samtools | Danecek et al.74 | https://github.com/samtools/samtools |
| featureCounts | Liao et al.75 | https://subread.sourceforge.net/ |
| DESeq2 | Love et al.76 | https://github.com/thelovelab/DESeq2 https://doi.org/10.18129/B9.bioc.DESeq2 |
| edgeR | Robinson et al.77 | https://bioinf.wehi.edu.au/edgeR/https://doi.org/10.18129/B9.bioc.edgeR |
| stats-package (R4.1.1) | R Core Team78 | https://www.r-project.org/ |
| Tslearn | Tavenard et al.46 | https://github.com/tslearn-team/tslearn/ |
| TensorFlow 2 | Abadi et al.79 | https://github.com/tensorflow/tensorflow https://doi.org/10.5281/zenodo.4724125 |
| Keras API | Chollet80 | https://github.com/keras-team/keras |
| scikit-learn | Pedregosa et al.81 | https://scikit-learn.org/stable/ |
| Google Colaboratory | https://colab.google/ | |
| XGboost | Chen and Guestrin51 | https://github.com/dmlc/xgboost |
| SHAP (SHapley Additive exPlanations) | Lundberg et al.82 | https://github.com/shap/shap |
| scikit-fda | Suárez et al.83 | https://github.com/GAA-UAM/scikit-fda |
| Uniform Manifold Approximation & Projection (UMAP) | McInnes et al.84 | https://github.com/lmcinnes/umap |
| MATLAB ODE simulation & optimization | The MathWorks Inc.85 | https://www.mathworks.com/ |