Browsing by Author "Wang, Congchao"

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    Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data
    Wang, Yinxue; Shi, Guilai; Miller, David J.; Wang, Yizhi; Wang, Congchao; Broussard, Gerard J.; Wang, Yue; Tian, Lin; Yu, Goquiang (Frontiers, 2017-07-14)
    Recent discoveries that astrocytes exert proactive regulatory effects on neural information processing and that they are deeply involved in normal brain development and disease pathology have stimulated broad interest in understanding astrocyte functional roles in brain circuit. Measuring astrocyte functional status is now technically feasible, due to recent advances in modern microscopy and ultrasensitive cell-type specific genetically encoded Ca²⁺ indicators for chronic imaging. However, there is a big gap between the capability of generating large dataset via calcium imaging and the availability of sophisticated analytical tools for decoding the astrocyte function. Current practice is essentially manual, which not only limits analysis throughput but also risks introducing bias and missing important information latent in complex, dynamic big data. Here, we report a suite of computational tools, called Functional AStrocyte Phenotyping (FASP), for automatically quantifying the functional status of astrocytes. Considering the complex nature of Ca²⁺ signaling in astrocytes and low signal to noise ratio, FASP is designed with data-driven and probabilistic principles, to flexibly account for various patterns and to perform robustly with noisy data. In particular, FASP explicitly models signal propagation, which rules out the applicability of tools designed for other types of data. We demonstrate the effectiveness of FASP using extensive synthetic and real data sets. The findings by FASP were verified by manual inspection. FASP also detected signals that were missed by purely manual analysis but could be confirmed by more careful manual examination under the guidance of automatic analysis. All algorithms and the analysis pipeline are packaged into a plugin for Fiji (ImageJ), with the source code freely available online at https://github.com/VTcbil/FASP.
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    Automated Tracking of Mouse Embryogenesis from Large-scale Fluorescence Microscopy Data
    Wang, Congchao (Virginia Tech, 2021-06-03)
    Recent breakthroughs in microscopy techniques and fluorescence probes enable the recording of mouse embryogenesis at the cellular level for days, easily generating terabyte-level 3D time-lapse data. Since millions of cells are involved, this information-rich data brings a natural demand for an automated tool for its comprehensive analysis. This tool should automatically (1) detect and segment cells at each time point and (2) track cell migration across time. Most existing cell tracking methods cannot scale to the data with such large size and high complexity. For those purposely designed for embryo data analysis, the accuracy is heavily sacrificed. Here, we present a new computational framework for the mouse embryo data analysis with high accuracy and efficiency. Our framework detects and segments cells with a fully probability-principled method, which not only has high statistical power but also helps determine the desired cell territories and increase the segmentation accuracy. With the cells detected at each time point, our framework reconstructs cell traces with a new minimum-cost circulation-based paradigm, CINDA (CIrculation Network-based DataAssociation). Compared with the widely used minimum-cost flow-based methods, CINDA guarantees the global optimal solution with the best-of-known theoretical worst-case complexity and hundreds to thousands of times practical efficiency improvement. Since the information extracted from a single time point is limited, our framework iteratively refines cell detection and segmentation results based on the cell traces which contain more information from other time points. Results show that this dramatically improves the accuracy of cell detection, segmentation, and tracking. To make our work easy to use, we designed a standalone software, MIVAQ (Microscopic Image Visualization, Annotation, and Quantification), with our framework as the backbone and a user-friendly interface. With MIVAQ, users can easily analyze their data and visually check the results.