Top-Down Inhibition (TDi) and Baseline Activation (BLa): Controlling Signal Transduction When Endogenous Cytokines...

Area of Science:

• Stem Cell Biology
• Developmental Biology
• Cell Signaling

Background:

• Human pluripotent stem cell (hPSC) research has advanced significantly, yielding diverse protocols for in vitro cell generation.
• Achieving specific and efficient differentiation requires precise control over signaling pathways, a persistent challenge in the field.
• The development of fully defined culture systems remains a key objective, necessitating robust methods to manage signaling variations.

Purpose of the Study:

• To present two complementary strategies for tightly regulating intermediate-strength signaling during hPSC differentiation.
• To demonstrate how these methods can mitigate endogenous signaling interference and facilitate serum-free culture.
• To highlight the applicability of these approaches beyond neural crest induction for various cell differentiation protocols.

Main Methods:

• Introduction of "top-down inhibition" and "baseline activation" techniques to modulate signaling pathways.
• Application of these methods to control Bone Morphogenetic Protein (BMP) and WNT signaling during neural crest induction.
• Transitioning from traditional bovine serum albumin-containing media to more defined culture systems.

Main Results:

• Successful delivery of tightly regulated, intermediate-strength signals crucial for specific cell fate determination.
• Maintenance of differentiation robustness and efficiency when switching to defined media, overcoming previous limitations.
• Demonstrated versatility of the approaches for various signaling contexts and differentiation systems.

Conclusions:

• Top-down inhibition and baseline activation offer powerful tools for precise control over hPSC differentiation.
• These methods facilitate the development of more defined and reproducible stem cell culture systems.
• The strategies presented have broad implications for advancing regenerative medicine and disease modeling using hPSCs.