Supplementary Materials

The PDF file includes:

  • Fig. S1. Experimental approaches and technical background data.
  • Fig. S2. ICA of untreated, LPS-treated, and PAL-treated cells and WGCNA gene coexpression module construction.
  • Fig. S3. Characterization of WGCNA-defined gene coexpression modules and corresponding pathways.
  • Fig. S4. The SOM-based approach uses whole transcriptome data to determine cell transcriptional states in response to LPS and PAL.
  • Fig. S5. SOM-based analysis of whole transcriptome single-cell profiles.
  • Fig. S6. Single-cell gene expression analysis of IL10, STAT3, and IL1B.
  • Fig. S7. Differential expression analysis of THP-1 cells deficient in MyD88.
  • Fig. S8. Key antagonistic transcriptional regulators are expressed in distinct transient cell populations.
  • Fig. S9. Cumulative expression proportion counted for each cell in the different treatments and states.
  • Fig. S10. Variance estimation of gene expression data derived from single cells: Part I.
  • Fig. S11. Variance estimation of gene expression data derived from single cells: Part II.
  • Fig. S12. Quality control of single-cell sequencing data.
  • Fig. S13. Microfluidic IFC microchamber screening.

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Other Supplementary Material for this manuscript includes the following:

  • Table S1 (Microsoft Excel format). WGCNA-based analysis of pathways and genes.
  • Table S2 (Microsoft Excel format). Output list of SOM-based analysis of pathways and genes.
  • Table S3 (Microsoft Excel format). A high number of genes specific for the LPS-induced, proinflammatory state that could be repressed by stable knockdown of MyD88.
  • Table S4 (Microsoft Excel format). List of primers.