Dataset Information

TRACES: a Freely Accessible, Semi-automated Pipeline for Detection, Tracking, and Quantification of Fluorescently Labeled Cellular Structures.

ABSTRACT: Subcellular structures exhibit diverse behaviors in different cellular processes, including changes in morphology, abundance, and relative spatial distribution. Faithfully tracking and quantifying these changes are essential to understand their functions. However, most freely accessible methods lack integrated features for tracking multiple objects in different spectral channels simultaneously. To overcome these limitations, we have developed TRACES (Tracking of Active Cellular Structures), a customizable and open-source pipeline capable of detecting, tracking, and quantifying fluorescently labeled cellular structures in up to three spectral channels simultaneously at single-cell level. Here, we detail step-by-step instructions for performing the TRACES pipeline, including image acquisition and segmentation, object identification and tracking, and data quantification and visualization. We believe that TRACES will be a valuable tool for cell biologists, enabling them to track and measure the spatiotemporal dynamics of subcellular structures in a robust and semi-automated manner.

SUBMITTER: Jiang X

PROVIDER: S-EPMC9516249 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

TRACES: a Freely Accessible, Semi-automated Pipeline for Detection, Tracking, and Quantification of Fluorescently Labeled Cellular Structures.

Jiang Xueer X Jiang Linhao L Jao Li-En LE

Bio-protocol 20220805 15

Subcellular structures exhibit diverse behaviors in different cellular processes, including changes in morphology, abundance, and relative spatial distribution. Faithfully tracking and quantifying these changes are essential to understand their functions. However, most freely accessible methods lack integrated features for tracking multiple objects in different spectral channels simultaneously. To overcome these limitations, we have developed TRACES (Tracking of Active Cellular Structures), a cu ...[more]

PMID: 36249859

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Project description:Recent advances in imaging technology and fluorescent probes have made it possible to gain information about the dynamics of subcellular processes at unprecedented spatiotemporal scales. Unfortunately, a lack of automated tools to efficiently process the resulting imaging data encoding fine details of the biological processes remains a major bottleneck in utilizing the full potential of these powerful experimental techniques. Here we present a computational tool, called PunctaSpecks, that can characterize fluorescence signals arising from a wide range of biological molecules under normal and pathological conditions. Among other things, the program can calculate the number, areas, life-times, and amplitudes of fluorescence signals arising from multiple sources, track diffusing fluorescence sources like moving mitochondria, and determine the overlap probability of two processes or organelles imaged using indicator dyes of different colors. We have tested PunctaSpecks on synthetic time-lapse movies containing mobile fluorescence objects of various sizes, mimicking the activity of biomolecules. The robustness of the software is tested by varying the level of noise along with random but known pattern of appearing, disappearing, and movement of these objects. Next, we use PunctaSpecks to characterize protein-protein interaction involved in store-operated Ca2+ entry through the formation and activation of plasma membrane-bound ORAI1 channel and endoplasmic reticulum membrane-bound stromal interaction molecule (STIM), the evolution of inositol 1,4,5-trisphosphate (IP3)-induced Ca2+ signals from sub-micrometer size local events into global waves in human cortical neurons, and the activity of Alzheimer's disease-associated β amyloid pores in the plasma membrane. The tool can also be used to study other dynamical processes imaged through fluorescence molecules. The open source algorithm allows for extending the program to analyze more than two types of biomolecules visualized using markers of different colors.

Project description:Biomolecular condensates are cellular organelles formed through liquid-liquid phase separation (LLPS) that play critical roles in cellular functions including signaling, transcription, translation, and stress response. Importantly, condensate misregulation is associated with human diseases, including neurodegeneration and cancer among others. When condensate-forming biomolecules are fluorescently-labeled and examined with fluorescence microscopy they appear as illuminated foci, or puncta, in cells. Puncta features such as number, volume, shape, location, and concentration of biomolecular species within them are influenced by the thermodynamics of biomolecular interactions that underlie LLPS. Quantification of puncta features enables evaluation of the thermodynamic driving force for LLPS and facilitates quantitative comparisons of puncta formed under different cellular conditions or by different biomolecules. Our work on nucleoporin 98 (NUP98) fusion oncoproteins (FOs) associated with pediatric leukemia inspired us to develop an objective and reliable computational approach for such analyses. The NUP98-HOXA9 FO forms hundreds of punctate transcriptional condensates in cells, leading to hematopoietic cell transformation and leukemogenesis. To quantify the features of these puncta and derive the associated thermodynamic parameters, we developed a live-cell fluorescence microscopy image processing pipeline based on existing methodologies and open-source tools. The pipeline quantifies the numbers and volumes of puncta and fluorescence intensities of the fluorescently-labeled biomolecule(s) within them and generates reports of their features for hundreds of cells. Using a standard curve of fluorescence intensity versus protein concentration, the pipeline determines the apparent molar concentration of fluorescently-labeled biomolecules within and outside of puncta and calculates the partition coefficient (Kp) and Gibbs free energy of transfer (ΔGTr), which quantify the favorability of a labeled biomolecule partitioning into puncta. In addition, we provide a library of R functions for statistical analysis of the extracted measurements for certain experimental designs. The source code, analysis notebooks, and test data for the Punctatools pipeline are available on GitHub: https://github.com/stjude/punctatools. Here, we provide a protocol for applying our Punctatools pipeline to extract puncta features from fluorescence microscopy images of cells.

Dataset Information

TRACES: a Freely Accessible, Semi-automated Pipeline for Detection, Tracking, and Quantification of Fluorescently Labeled Cellular Structures.

Publications

TRACES: a Freely Accessible, Semi-automated Pipeline for Detection, Tracking, and Quantification of Fluorescently Labeled Cellular Structures.

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