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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2019)

High-throughput optical sensing and imaging instrument for capture and analysis of cells are among the most essential tools for biomedical applications. While optical microscopy is one of the most widely used methods for unveiling the molecular composition of biological specimens, flow cytometry is the gold standard high-throughput tool for single-cell characterization in numerous biomedical and clinical applications. The strong desire to fill in the technological gap between the high-content fluorescence microscope and high-throughput flow cytometer has inspired us to develop a cell analyzer platform with three-dimensional (3D) imaging capabilities.

This dissertation details the development of a 3D imaging flow cytometer, in which microfluidics, acousto-optical, optical and electronic systems are integrated. Based on one enabling technology, namely spatial-temporal transformation, we first developed a novel high-throughput two-dimensional imaging tool for flow cytometry applications. Instead of replacing the single-pixel photodetectors commonly used in flow cytometry with mega-pixelated cameras, this technology uses mathematical algorithms and a specially designed spatial filter to give flow cytometers capabilities of high-throughput, motion-blur-free, multi-color, and multi-mode imaging of single cells. By virtue of the simplicity, high flexibility, and high scalability of this technology, we then combined it with optical sectioning and high-speed laser scanning techniques to enable 3D fluorescence and 90-degree side-scattering imaging of single cells at flow speeds as high as a meter per second. The cameraless 3D imaging flow cytometry uses multiple scanning techniques to add spatial information in a fairly conventional flow cytometry architecture amenable to wide adoption. By precisely mapping time to space, photodetector readout at one timepoint corresponds to one voxel in a 3D space. Through experiments on various biological samples, we demonstrate the functionality of the 3D imaging flow cytometry platform.

As big data create a bonanza for scientists and image-based cell sorter has been brought about, such a high-throughput 3D imaging flow cytometry system will be an enabler to connect phenotype and genotype studies in the fields of immunology, oncology, cell- and gene- therapy, and drug discovery, where heterogeneity is recognized.

    Cover page: Cameraless High-throughput Imaging Flow Cytometry for Biomedical Applications
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2016)
    High-throughput single cell imaging is a critical enabling and driving technology in molecular and cellular biology, biotechnology, medicine and related areas. Imaging flow cytometry combines the single-cell imaging capabilities of microscopy with the high-throughput capabilities of conventional flow cytometry. Recent advances in imaging flow cytometry are remarkably revolutionizing single-cell analysis. This article describes recent imaging flow cytometry technologies and their challenges.
      Cover page: Review: imaging technologies for flow cytometry
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2016)
      Although a flow cytometer, being one of the most popular research and clinical tools for biomedicine, can analyze cells based on the cell size, internal structures such as granularity, and molecular markers, it provides little information about the physical properties of cells such as cell stiffness and physical interactions between the cell membrane and fluid. In this paper, we propose a computational cell analysis technique using cells' different equilibrium positions in a laminar flow. This method utilizes a spatial coding technique to acquire the spatial position of the cell in a microfluidic channel and then uses mathematical algorithms to calculate the ratio of cell mixtures. Most uniquely, the invented computational cell analysis technique can unequivocally detect the subpopulation of each cell type without labeling even when the cell type shows a substantial overlap in the distribution plot with other cell types, a scenario limiting the use of conventional flow cytometers and machine learning techniques. To prove this concept, we have applied the computation method to distinguish live and fixed cancer cells without labeling, count neutrophils from human blood, and distinguish drug treated cells from untreated cells. Our work paves the way for using computation algorithms and fluidic dynamic properties for cell classification, a label-free method that can potentially classify over 200 types of human cells. Being a highly cost-effective cell analysis method complementary to flow cytometers, our method can offer orthogonal tests in companion with flow cytometers to provide crucial information for biomedical samples.
        Cover page: Computational cell analysis for label-free detection of cell properties in a microfluidic laminar flow
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2021)
        Insulators play a critical role in spatiotemporal gene regulation in animals. The evolutionarily conserved CCCTC-binding factor (CTCF) is required for insulator function in mammals, but not all of its binding sites act as insulators. Here we explore the sequence requirements of CTCF-mediated transcriptional insulation using a sensitive insulator reporter in mouse embryonic stem cells. We find that insulation potency depends on the number of CTCF-binding sites in tandem. Furthermore, CTCF-mediated insulation is dependent on upstream flanking sequences at its binding sites. CTCF-binding sites at topologically associating domain boundaries are more likely to function as insulators than those outside topologically associating domain boundaries, independently of binding strength. We demonstrate that insulators form local chromatin domain boundaries and weaken enhancer-promoter contacts. Taken together, our results provide genetic, molecular and structural evidence connecting chromatin topology to the action of insulators in the mammalian genome.
          Cover page: CTCF mediates dosage- and sequence-context-dependent transcriptional insulation by forming local chromatin domains
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2024)
          The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell-cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene-gene colocalization. We demonstrate MERFISH, seqFISH + , Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.
            Cover page: Bento: a toolkit for subcellular analysis of spatial transcriptomics data
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