Assessing EP receptor signatures in PBMC’s


Biochemical measurements obtained from the plasma of the different clinical cohorts can be compared with one another. As an initial discovery approach quantification of PTGER1–PTGER4 transcripts (mRNA) in whole PBMCs would already be highly informative, particularly if distinct PTGER transcript signatures correlate with the cytokine clusters previously identified in the cohort. If such signatures are observed, RT-qPCR analysis of PTGER transcripts in sorted monocytes and CD4⁺ T cells could subsequently be performed to determine whether the PBMC signature reflects intrinsic receptor regulation within specific immune cell populations, changes in the composition of the circulating immune compartment, or a combination of both.

To independently assess T-helper cell polarization, the lineage-defining transcription factors TBX21, GATA3, RORC, and FOXP3 could be quantified by RT-qPCR in parallel. If transcript-level differences are confirmed, these findings could subsequently be validated at the protein level by intracellular flow cytometric staining for T-bet, GATA3, RORγt, and FOXP3.

Functional receptor studies would only be considered after transcript and protein differences have been established.

Experimental strategy (not all phases have to be included)

Phase I – PTGER transcript signatures of circulating PBMCs

Measure PTGER1–PTGER4 transcripts in total PBMCs using RT-qPCR

Beginning with whole PBMCs provides a rapid, inexpensive and unbiased assessment of the circulating immune system. The balance among PTGER1–PTGER4 transcripts reflects the immune state of the patient. If PTGER transcript signatures prove informative, progressively more detailed analyses can identify the cellular origin and functional significance of these signatures.

This approach maximizes information gained while minimizing experimental complexity and cost.

Objective:

Determine whether distinct PTGER transcript signatures are associated with previously identified Long COVID immune subtypes and cytokine-clusters.

Phase II – PTGER transcript signatures in sorted monocytes and CD4⁺ T cells1

Measure RT-qPCR of PTGER1–PTGER4 in sorted monocytes and CD4⁺ T cells using RT-qPCR

Objective:

Determine whether distinct PTGER transcript signatures are stimulated by specific subsets of immune cells

Phase III – Measuring factors that define Th subsets2

To independently assess T-helper cell polarization, lineage-defining transcription factors (TBX21, GATA3, RORC and FOXP3) could be quantified by RT-qPCR. If distinct PTGER transcript signatures are identified, these findings may subsequently be validated at the protein level by intracellular flow cytometric staining for T-bet, GATA3, RORγt and FOXP3.

  1. RT-qPCR analysis of T-helper cell lineage-specific transcription factors

TBX21 (T-bet) → Th1

GATA3 → Th2

RORC (RORγt) → Th17

FOXP3 → Treg

2. Flow cytometry

Staining intracellularly for:

T-bet
GATA3
RORγt
FOXP3

after first identifying CD4⁺ T cells.

Objective:

Determine whether PTGER transcript signatures correlate with T-helper cell polarization

Phase IV – EP receptor protein expression profile3

Measure EP receptor protein expression using flow cytometry. Analyze individual immune cell subsets (e.g. CD14⁺ monocytes, CD4⁺ T cells, CD8⁺ T cells and NK cells)

Objective:

Determine whether transcript differences correspond to altered receptor abundance and phenotypes.

Phase V – signaling assays4 

Perform functional signaling assays following stimulation with PGE2 or selective EP receptor agonists.

Objective:

Determine whether differences in transcript or protein expression translate into altered receptor signaling capacity.

Table 4. Overview of technical options

RNA extraction

Standard kit

Total RNA

Reverse transcription

cDNA synthesis

cDNA

RT-qPCR1

(qRT-PCR) in whole PMBC’s

PTGER1–PTGER4

Is there a PTGER transcript signature?

after cell sorting

MACS or FACS

Monocytes, CD4⁺ T cells, optionally NK cells

Northern blot analysis5

(used before qPCR became standard)

EP transcripts in immune cells

RT-qPCR analysis of T-helper cell lineage-specific transcription factors 2,6-9

PBMC

Is the PBMC compartment enriched for Th1-, Th2-, Th17-, (or Treg) associated programs?

Flow cytometry7

CD4⁺ T cells

Do Th-cell signatures emerge?

scRNA-seq1,10

(expensive)

Single cell

Which cell types generate the PTGE signature?

Western blot3

Measures total receptor protein, good for cultured monocytes

Cannot distinguish which immune cell expresses the protein

Flow cytometry3

EP-receptors on monocytes (if validated antibodies are available)

Are the receptors actually present on the cell surface?

Functional assays4

cAMP, calcium, β-arrestin recruitment

Do the receptors signal differently?

1             Kashmiry, A., Tate, R., Rotondo, G., Davidson, J. & Rotondo, D. The prostaglandin EP4 receptor is a master regulator of the expression of PGE(2) receptors following inflammatory activation in human monocytic cells. Biochim Biophys Acta Mol Cell Biol Lipids 1863, 1297-1304 (2018). https://doi.org/10.1016/j.bbalip.2018.07.003

2             Wang, Y., Su, M. A. & Wan, Y. Y. An essential role of the transcription factor GATA-3 for the function of regulatory T cells. Immunity 35, 337-348 (2011). https://doi.org/10.1016/j.immuni.2011.08.012

3             Klasen, C. et al. Prostaglandin receptor EP4 expression by Th17 cells is associated with high disease activity in ankylosing spondylitis. Arthritis Res Ther 21, 159 (2019). https://doi.org/10.1186/s13075-019-1948-1

4             Lone, A. M. et al. Systems approach reveals distinct and shared signaling networks of the four PGE. Sci Signal 14, eabc8579 (2021). https://doi.org/10.1126/scisignal.abc8579

5             Mori, K. et al. Gene expression of the human prostaglandin E receptor EP4 subtype: differential regulation in monocytoid and lymphoid lineage cells by phorbol ester. J Mol Med (Berl) 74, 333-336 (1996). https://doi.org/10.1007/bf00207510

6             Sadeghalvad, M., Khijakadze, D., Orangi, M. & Takei, F. Flow cytometric analysis of innate lymphoid cells: challenges and solutions. Frontiers in Immunology Volume 14 – 2023 (2023). https://doi.org/10.3389/fimmu.2023.1198310

7             Cossarizza, A. et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies. European Journal of Immunology 47, 1584-1797 (2017). https://doi.org/https://doi.org/10.1002/eji.201646632

8             Yu, F., Sharma, S., Edwards, J., Feigenbaum, L. & Zhu, J. Dynamic expression of transcription factors T-bet and GATA-3 by regulatory T cells maintains immunotolerance. Nat Immunol 16, 197-206 (2015). https://doi.org/10.1038/ni.3053

9             Lu, Y. et al. Expansion of CD4+CD25+ and CD25- T-Bet, GATA-3, Foxp3 and RORγt Cells in Allergic Inflammation, Local Lung Distribution and Chemokine Gene Expression. PLOS ONE 6, e19889 (2011). https://doi.org/10.1371/journal.pone.0019889

10           Punyawatthananukool, S. et al. Prostaglandin E2-EP2/EP4 signaling induces immunosuppression in human cancer by impairing bioenergetics and ribosome biogenesis in immune cells. Nature Communications 15, 9464 (2024). https://doi.org/10.1038/s41467-024-53706-3

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