Crystal Chen

All Publications

• High-resolution dynamic imaging of chromatin DNA communication using Oligo-LiveFISH. Cell Zhu, Y., Balaji, A., Han, M., Andronov, L., Roy, A. R., Wei, Z., Chen, C., Miles, L., Cai, S., Gu, Z., Tse, A., Yu, B. C., Uenaka, T., Lin, X., Spakowitz, A. J., Moerner, W. E., Qi, L. S. 2025

  Abstract

  Three-dimensional (3D) genome dynamics are crucial for cellular functions and disease. However, real-time, live-cell DNA visualization remains challenging, as existing methods are often confined to repetitive regions, suffer from low resolution, or require complex genome engineering. Here, we present Oligo-LiveFISH, a high-resolution, reagent-based platform for dynamically tracking non-repetitive genomic loci in diverse cell types, including primary cells. Oligo-LiveFISH utilizes fluorescent guide RNA (gRNA) oligo pools generated by computational design, in vitro transcription, and chemical labeling, delivered as ribonucleoproteins. Utilizing machine learning, we characterized the impact of gRNA design and chromatin features on imaging efficiency. Multi-color Oligo-LiveFISH achieved 20-nm spatial resolution and 50-ms temporal resolution in 3D, capturing real-time enhancer and promoter dynamics. Our measurements and dynamic modeling revealed two distinct modes of chromatin communication, and active transcription slows enhancer-promoter dynamics at endogenous genes like FOS. Oligo-LiveFISH offers a versatile platform for studying 3D genome dynamics and their links to cellular processes and disease.

  View details for DOI 10.1016/j.cell.2025.03.032

  View details for PubMedID 40239646

• Precision Transcriptome Editing. ACS synthetic biology Chen, C., Qi, L. S. 2024

  Abstract

  Manipulating RNA species in mammalian cells has emerged as an important strategy for precise gene expression control. Here we review recent advances in precision transcriptome editing with a focus on tools that engineer specific transcripts for abundance, translation, base editing, alternative isoforms, and chemical modifications. While some of these methods have demonstrated efficiency in therapeutically relevant cellular or in vivo models, most require further study on their clinical safety and efficacy. Precision transcriptome engineering holds great potential for both mechanistic study of RNA biology and future gene and cell-based therapeutic applications.

  View details for DOI 10.1021/acssynbio.4c00183

  View details for PubMedID 39435985

• A versatile CRISPR-Cas13d platform for multiplexed transcriptomic regulation and metabolic engineering in primary human T cells. Cell Tieu, V., Sotillo, E., Bjelajac, J. R., Chen, C., Malipatlolla, M., Guerrero, J. A., Xu, P., Quinn, P. J., Fisher, C., Klysz, D., Mackall, C. L., Qi, L. S. 2024

  Abstract

  CRISPR technologies have begun to revolutionize T cell therapies; however, conventional CRISPR-Cas9 genome-editing tools are limited in their safety, efficacy, and scope. To address these challenges, we developed multiplexed effector guide arrays (MEGA), a platform for programmable and scalable regulation of the T cell transcriptome using the RNA-guided, RNA-targeting activity of CRISPR-Cas13d. MEGA enables quantitative, reversible, and massively multiplexed gene knockdown in primary human T cells without targeting or cutting genomic DNA. Applying MEGA to a model of CAR T cell exhaustion, we robustly suppressed inhibitory receptor upregulation and uncovered paired regulators of T cell function through combinatorial CRISPR screening. We additionally implemented druggable regulation of MEGA to control CAR activation in a receptor-independent manner. Lastly, MEGA enabled multiplexed disruption of immunoregulatory metabolic pathways to enhance CAR T cell fitness and anti-tumor activity in vitro and in vivo. MEGA offers a versatile synthetic toolkit for applications in cancer immunotherapy and beyond.

  View details for DOI 10.1016/j.cell.2024.01.035

  View details for PubMedID 38387457

• Development of a Quorum-Sensing Based Circuit for Control of Coculture Population Composition in a Naringenin Production System. ACS synthetic biology Dinh, C. V., Chen, X., Prather, K. L. 2020; 9 (3): 590-597

  Abstract

  As synthetic biology and metabolic engineering tools improve, it is feasible to construct more complex microbial synthesis systems that may be limited by the machinery and resources available in an individual cell. Coculture fermentation is a promising strategy for overcoming these constraints by distributing objectives between subpopulations, but the primary method for controlling the composition of the coculture of production systems has been limited to control of the inoculum composition. We have developed a quorum sensing (QS)-based growth-regulation circuit that provides an additional parameter for regulating the composition of a coculture over the course of the fermentation. Implementation of this tool in a naringenin-producing coculture resulted in a 60% titer increase over a system that was optimized by varying inoculation ratios only. We additionally demonstrated that the growth control circuit can be implemented in combination with a communication module that couples transcription in one subpopulation to the cell-density of the other population for coordination of behavior, resulting in an additional 60% improvement in naringenin titer.

  View details for DOI 10.1021/acssynbio.9b00451

  View details for PubMedID 32040906

• General Method for the Identification of Crystal Faces Using Raman Spectroscopy Combined with Machine Learning and Application to the Epitaxial Growth of Acetaminophen. Langmuir : the ACS journal of surfaces and colloids Wijethunga, T. K., Stojaković, J., Bellucci, M. A., Chen, X., Myerson, A. S., Trout, B. L. 2018; 34 (33): 9836-9846

  Abstract

  Crystal morphology is one of the key crystallographic characteristics that governs the macroscopic properties of crystalline materials. The identification of crystal faces, or face indexing, is an important technique that is used to get information regarding a crystal's morphology. However, it is mainly limited to single crystal X-ray diffraction (SCXRD) and it is often not applicable to products of routine crystallizations becasue it requires high quality single crystals in a narrow size range. To overcome the limitations of the SCXRD method, we have developed a robust and convenient Raman face indexing method based on work by Moriyama et al. This method exploits small but detectable differences in Raman spectra of crystal faces caused by different orientations of the crystallographic axis relative to the direction and polarization of the excitation laser beam. The method requires the compilation of a Raman spectral library for each compound and must be built and validated by SCXRD face indexing. Once the spectral library is available for a compound, the identity of unknown crystal faces (from any crystal that is larger than laser beam) can be inferred by collecting and comparing the Raman spectra to spectra within the library. We have optimized this approach further by developing a machine-learning algorithm that identifies crystal faces by performing a statistical comparison of the spectra in the Raman library and the Raman spectra of the unknown crystal faces. Here, we report the development of the Raman face indexing method and apply it to three different epitaxial systems: Acetaminophen (APAP) grown as an overlayer crystal on d-mannitol (MAN), d-galactose (GAL), and xylitol (XYL) substrates. For each of these epitaxial systems, the crystals were grown under various experimental conditions and have a wide range of sizes and quality. Using the Raman face indexing method, we were able to perform high-throughput indexing of a large number of crystals from different crystallization conditions, which could not be achieved using SCXRD or other analytical techniques.

  View details for DOI 10.1021/acs.langmuir.8b01791

  View details for PubMedID 30053784