Biology subjects

Toppen, J.

Publications and source records attributed to Toppen, J..

3 recordsLinked to original sources

bioRxiv2026

Live-cell imaging of enhancer-promoter dynamics reveals transient contact-driven gene activation

Enhancers are key regulators of mammalian gene expression, yet how they interact with promoters in space (contact vs. action-at-a-distance) and in time (transient vs. stable) remains poorly understood. Recent studies suggest that enhancers can activate promoters across distances exceeding 200 nanometers, challenging classical contact models, but limited spatiotemporal resolution has obscured the mechanistic details of enhancer-promoter (E-P) interactions and their link to transcription. Here, we engineered a synthetic biology platform optimized for the simultaneous visualization of E-P 3D distance and nascent transcription using super-resolution live-cell imaging. By applying five complementary approaches integrating imaging, 3D genomics, and gene expression data across cell lines, we estimate that transcriptional activation is mediated by [~]25-42 nanometer contacts on the seconds timescale. Our results support a transient contact mechanism for E-P-mediated gene activation.

biophysics↗

bioRxiv2025

Noise-driven morphogenesis independent of transcriptional regulatory programs

Development is widely understood as a deterministic process driven by transcriptional programs that specify cell fate and orchestrate morphogenesis. However, this view overlooks pervasive stochasticity in gene expression, often considered an obstacle to reliable tissue patterning. Here, we introduce stochastic tuning-driven morphogenesis (STM), an alternative conceptualization of development in which noisy gene expression is not a nuisance but the primary driving force--guiding cell fates toward optimal multicellular configurations by a trial-and-error process analogous to reinforcement learning. STM operates independently of fixed transcriptional programs, instead relying on convergence of sensory information into signaling hubs, which by reinforcing random transcriptional changes, prospectively and contextually fine-tune gene expression along key developmental milestones. STM offers a fundamentally different view of development--one in which stochastic gene expression enables real-time optimization of gene expression toward multicellular objectives, implementing a self-organizing process that is inherently resistant to molecular and environmental fluctuations.

developmental biology↗

bioRxiv2024

Topological data analysis of pattern formation of human induced pluripotent stem cell colonies

Understanding the multicellular organization of stem cells is vital for determining the mechanisms that coordinate cell fate decision-making during differentiation; these mechanisms range from neighbor-to-neighbor communication to tissue-level biochemical gradients. Current methods for quantifying multicellular patterning cannot capture the spatial properties of cell colonies across all scales and typically rely on human annotation or a priori selection of parameters. We present a computational pipeline that utilizes topological data analysis to generate quantitative, multiscale descriptors which capture the shape of data extracted from multichannel microscopy images. By applying our pipeline to certain stem cell colonies, we detected subtle differences in patterning that reflect distinct biological markers and progressive stages of differentiation. These results yield insight into directed cellular movement and morphogen-mediated, neighbor-to-neighbor signaling. Because of its broad applicability to immunofluorescence microscopy images, our pipeline is well-positioned to serve as a general-purpose tool for the quantitative study of multicellular pattern formation.

developmental biology↗