MicroLive: An Image Processing Toolkit for Quantifying Live-cell Single-Molecule Microscopy

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Aguilera , View ORCID Profile William S. Raymond , Rhiannon M. Sears , Nathan L. Nowling , Brian Munsky , View ORCID Profile Ning Zhao doi: https://doi.org/10.1101/2025.09.25.678587 Luis U. Aguilera 1 Department of Biochemistry and Molecular Genetics , University of Colorado-Anschutz Medical Campus, 80045, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site William S. Raymond 2 School of Biomedical and Chemical Engineering, Colorado State University , 80523, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for William S. Raymond Rhiannon M. Sears 1 Department of Biochemistry and Molecular Genetics , University of Colorado-Anschutz Medical Campus, 80045, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nathan L. Nowling 1 Department of Biochemistry and Molecular Genetics , University of Colorado-Anschutz Medical Campus, 80045, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brian Munsky 2 School of Biomedical and Chemical Engineering, Colorado State University , 80523, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ning Zhao 1 Department of Biochemistry and Molecular Genetics , University of Colorado-Anschutz Medical Campus, 80045, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ning Zhao Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Advances in live-cell fluorescence microscopy have enabled us to visualize single molecules (such as mRNAs and nascent proteins) in real time with high spatiotemporal resolution. However, these experiments generate large datasets that require complex computational processing pipelines to derive meaningful and quantitative information, which is a technical barrier for many researchers. To address this barrier, here, we introduce MicroLive , an open-source Python-based application for quantifying live-cell microscopy images. MicroLive provides an interactive Graphical User Interface (GUI) to perform key tasks, including cell segmentation, photo-bleaching correction, single-particle detection/tracking, spot intensity quantification, inter-channel colocalization, and time-series correlation analysis. As a ground-truth testing dataset, we used synthetic live-cell imaging data generated with the rSNAPed toolkit, demonstrating accurate extraction of biologically relevant parameters. Microscopy images of U-2 OS cells expressing a gene construct smHA-KDM5B-BoxB-MS2 were used to demonstrate the use of this software. Availability and implementation MicroLive is distributed under a GPLv3 license and available on GitHub. https://github.com/ningzhaoAnschutz/microlive . Contact ning.zhao{at}cuanschutz.edu . 1 Introduction Live-cell single-molecule microscopy is transforming our understanding of gene expression ( 1 ). Novel imaging strategies combine advanced live-cell fluorescence microscopy and genetically encoded fluorescent tagging systems, such as intrabody systems (e.g., SunTag ( 2 ; 3 ) and frankenbodies ( 4 ; 5 )) and RNA stem-loop systems (e.g., MS2 ( 6 ) and PP7 stem loops ( 7 )), allowing researchers to visualize nascent proteins and mRNAs directly in real time in live cells ( 8 ). These technological advances have revealed complex phenomena, such as single mRNA translation ( 3 ; 9 ; 10 ; 11 ; 12 ), translation bursts ( 13 ), frameshifting ( 14 ), and IRES-initiated translation ( 15 ). However, extracting meaningful information from live-cell microscopy is a complicated multi-step process, including cell segmentation, diffraction-limited fluorescent spots detection, linking detected spots across frames, quantifying spot intensity, and determining colocalization or temporal correlations between imaging channels ( 16 ). Performing these analyses often requires a combination of software tools (e.g., ImageJ ( 17 )) and custom scripts, which creates a technical barrier for many researchers. To address this, we introduce MicroLive , a user-friendly Graphical User Interface (GUI) platform for live-cell single-molecule imaging analysis. It enables users to load multi-dimensional images and implement a complete image-processing pipeline through a point-and-click graphical interface, as shown in Figure 1 . Download figure Open in new tab Figure 1: A snapshot of MicroLive . Multiple tabs allow users to perform cell segmentation, spot detection, particle tracking, intensity calculation, colocalization, and correlation analysis. The processed image represents a U-2 OS cell expressing the smHA-KDM5B-BoxB-MS2 gene construct. 2 Method and Implementation MicroLive is implemented in Python with a modular architecture: the core image processing routines are coded as classes and reside in src/microscopy.py , while the code generating the GUI is located in gui/micro.py . MicroLive applies parallel computing to accelerate compute-intensive tasks, such as processing multiple-frame images. Additionally, the GUI loads on-demand individual 2D frames or 3D stacks from the disk only when the frame or stack is displayed or processed, then frees the memory immediately. This on-demand strategy minimizes memory usage and supports large datasets. MicroLive supports standard microscopy files like multi-dimensional TIFF or LIF. For unstructured TIFF files, a Jupyter Notebook is provided to convert them into the standard format used in the GUI for downstream image analyses. Once loaded in MicroLive , microscopy images are internally converted into uint16 NumPy arrays. As a pre-processing step, the user can apply an exponential decay to correct the loss of fluorescence intensity due to photobleaching ( 18 ). Segmentation employs either a manual outline tool that allows users to draw Regions of Interest (ROI) or a watershed method to define ROIs ( 19 ). Masks are overlaid on images for validation and used to map detected spots to individual cells. Particles or spots are detected using the TrackPy library ( 20 ) for 2D images or Big-FISH ( 21 ) for 3D images. Detected particles can be labeled as clusters if their size is multiple times larger than the user-defined particle size. Users can filter out large clusters that may represent aggregates. Particle trajectories are constructed using a nearest-neighbor algorithm with tunable displacement and memory parameters. Particle linking is supported for both 2D and 3D images. Importantly, the code offers visualization of the linked trajectories and the particle identifier, allowing users to detect and correct tracking errors in real-time. For multi-color images, MicroLive allows automated methods to detect colocalized spots by using a convolutional neural network ( 22 ) trained on manually-annotated images combined with augmented synthetic data to predict the presence of particles in a given ROI. Automated colocalization can be manually curated to reduce false positives and false negatives. Fluorescence intensities for the detected spots can be extracted over time using multiple methods, including total intensity, Gaussian fits, and background subtraction methods. MicroLive computes auto- and cross-correlation functions to reveal kinetic parameters such as intensity fluctuations, dwell times, and inter-signal delays ( 23 ). All data generated by the GUI can easily be exported as CSV files for downstream processing. Metadata containing all the parameters and thresholds used during the image processing steps is automatically generated and exported to ensure reproducibility. A complete description of the methods used in MicroLive is given in the Supplementary File. 3 Validation and Results To verify our code with a ground truth dataset, we used synthetic movies generated with rSNAPed ( 24 ). For this, a synthetic dataset containing 360 frames with a 5-sec frame interval, two color channels, 512 × 512 pixels, and 80 mRNA spots was generated to model mRNA translation using an initiation rate of 0.04 1/sec and an elongation rate of 5 aa/sec. Photobleaching was simulated using a decreasing exponential function with a decay rate of 0.001 1/sec. Ground-truth positions and intensities were retained for benchmarking, and recovered results are provided in Supplementary Figure S2, showing a strong agreement between the tracked particles and the values used for the simulation. For example, MicroLive accurately detected more than 70 mRNA spots at all time points. MicroLive estimated an intensity decay of 0.001 1/sec. Additionally, to determine whether MicroLive can correctly extract temporal intensity from the images, we calculated the autocorrelation function of nascent protein intensity traces and extracted initiation and elongation rates as described by Larson et al. ( 25 ). We obtained a de-correlation time of ≈ 365 sec, corresponding to an elongation rate of 5.2 aa/sec, and a value for the autocorrelation function at lag zero G (0) = 0.07, corresponding to an initiation rate of 0.038 1/sec. A side-by-side comparison between synthetic data generated with the rSNAPed library and MicroLive recovered values is given in the Supplementary File Table S1 and Figure S2. To test MicroLive , we analyzed microscopy translation images of a gene construct smHA-KDM5B-BoxB-MS2 ( 26 ) in live U-2 OS cells. The smHA-KDM5B-BoxB-MS2 (plasmid sequence is provided) construct consists of an N-terminal spaghetti monster HA (smHA) tag ( 27 ) (including 10x HA tags) fused to our protein-of-interest KDM5B, 15x BoxB ( 26 ; 28 ; 29 ) and 24x MS2 stem-loops ( 6 ) in the 3’ untranslated region (UTR). Translation spots were identified by colocalized nascent protein spots visualized by anti-HA-frankenbody-HaloTag ( 4 ) stained with JF646 dyes and mRNA spots labeled by tandem MS2 Coat Protein (tdMCP) ( 6 ) fused with tandem monomeric StayGold (tdmSG) ( 30 ; 31 ). The mRNA spots were tethered to the plasma membrane through the interaction between λ N-CAAX and the BoxB stem-loops ( 29 ). The translation images were collected using a Leica Stellaris 5 confocal microscope with a 63x oil objective for 600 frames at 1 frame per second (fps) rate with a single z plane. Images were loaded into the GUI to perform cell segmentation, spot detection, intensity calculation, and correlation analyses. The complete quantification is presented in the Supplementary File. 4 Conclusion MicroLive is a toolbox to quantify single-molecule microscopy images. MicroLive unifies commonly used image processing tasks such as cell segmentation, spot detection, particle tracking, co-localization, and correlation analyses within a single accessible platform. The user-friendly GUI platform lowers the barrier for non-programmers to perform complex image-processing tasks. MicroLive is open-source with a GPLv3 license. 5 Competing interests No competing interest is declared. Funding LUA, RMS, NLN, and NZ were supported by NIH award R00GM141453 and Cystic Fibrosis Foundation award 005749A123. WSR and BM were supported by NIH award R35GM124747. Acknowledgments Plasmid smHA-KDM5B-BoxB-MS2 was kindly provided by Gabriel Galindo in the Stasevich lab at Colorado State University. Funder Information Declared National Institutes of Health, https://ror.org/01cwqze88 , R00GM141453 Cystic Fibrosis Foundation, https://ror.org/00ax59295 , 005749A123 National Institutes of Health, https://ror.org/01cwqze88 , R35GM124747 Footnotes https://github.com/ningzhaoAnschutz/microlive References [1]. ↵ Tatsuya Morisaki , O’Neil Wiggan , and Timothy J. Stasevich . Translation dynamics of single mRNAs in live cells . Annual Review of Biophysics . 53 ( 2024 ), pages 65 – 85 . 2024 . OpenUrl PubMed [2]. ↵ Marvin E. Tanenbaum , Luke A. Gilbert , Lei S. Qi , Jonathan S. Weissman , and Ronald D. Vale . A protein-tagging system for signal amplification in gene expression and fluorescence imaging . Cell ., 159 ( 3 ), pages 635 – 646 . 2014 . 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