Quantitative analysis of subcellular distributions with an open-source, object-based tool

Microscopy imaging is a foundational technique for cell biology, as it allows for the interrogation of spatial relationships between cellular and tissue components and reveals previously unrecognized aspects of biological function (Thorn, 2016). As a general principle, the subcellular localization of biological components to specific domains is a crucial determinant of function, as spatial proximity allows for interactions among molecules, proteins, and organelles (Yang, 2013). Thus, researchers commonly investigate the localization of cellular components relative to each other using microscopy imaging to gain insights into biological function (Lagache et al., 2015). Multiple techniques detect interactions between molecules on the nanometer scale, including fluorescent resonance energy transfer (Zadran et al., 2012), fluorescence cross-correlation spectroscopy (Fitzpatrick and Lillemeier, 2011), bimolecular protein complementation (Kodama and Hu, 2012), and the proximity ligation assay (Söderberg et al., 2006). While powerful, these techniques often require specialized reagents and/or analysis and cannot detect other biologically relevant phenomena, such as indirect interactions within macromolecular complexes and direct interactions between large molecules (Lagache et al., 2015). For these reasons, investigation of the relationship between fluorescence signals remains an informative approach to infer biologically relevant interactions in fixed or live samples (Aaron et al., 2018).

An important challenge for microscopists is to extract meaningful quantitative information from data-rich images. Manual quantification of images is prone to user bias, time-intensive, may require specialized training, and may be less reproducible (Sbalzarini, 2016). Automated image analysis approaches are favored due to their reduced user bias, increased reproducibility, and ability to detect low frequency or subtle changes (Wiesmann et al., 2015).

Quantification of the relationship between fluorescence signals is referred to as colocalization analysis (Aaron et al., 2018). Colocalization analysis tests the hypothesis that two fluorescently labelled objects localize in a biological specimen within the resolution limit of the optical system (Cordelières and Bolte, 2014). In general, there are two approaches to test colocalization: methods based on the relationship between pixel intensities and methods based on the relationship between groups of pixels termed objects (Cordelières and Bolte, 2014). Pixel-based methods test for correlation between pixel intensities or spatial overlap of non-background pixels and report the degree of signal coincidence using numerical values termed colocalization coefficients (Bolte and Cordelières, 2006). However, pixel-based approaches to quantify colocalization are unable to detect signals in close physical proximity that do not overlap. Object-based approaches, on the other hand, detect the distances between objects and can reveal biologically relevant distinctions between biological conditions that may be missed by pixel-based techniques (Cordelières and Bolte, 2014).

Despite the sensitivity of object-based methods, most image analysis software platforms focus on pixel-based methods for colocalization analysis, including CellProfiler and ImageJ (McQuin et al., 2018; Schindelin et al., 2015). The JaCOP plugin for ImageJ includes object-based colocalization, but distance measurements can only be made between the centers of objects (Cordelières and Bolte, 2014). Distances between objects can be measured using CellProfiler, but the software does not provide tools to visualize the data (McQuin et al., 2018). Similarly, the Distance Transformation feature of Imaris Image Analysis Software (Imaris; Bitplane, Inc.) is a popular tool to measure distances between objects in three-dimensions (x, y, z; 3D), but this commercial software is cost prohibitive for some users. In addition, tools to visualize the resultant measurements are not included. Thus, the cell biology community currently lacks an open-source, customizable, and user-friendly tool to assess the distance between objects detected by fluorescence microscopy.

Here, we present the SubcellularDistribution pipeline, an automated and generalizable method to assess relationships between fluorescently labeled subcellular structures based on distance measurements between objects in 3D. This pipeline includes a single molecule normalization module that can be used to estimate the number of molecules per object for single molecule data, including single molecule RNA fluorescence in situ hybridization (smFISH) data. In addition, the SubcellularDistribution pipeline includes tools for data visualization. We compare our method to commercially available image analysis software and demonstrate its utility in analyzing the distribution of smFISH signals relative to two subcellular compartments, the centrosome and the nucleus. We share the SubcellularDistribution pipeline as free and open-source software with the cell biology community to facilitate robust, object-based quantification of colocalization in fluorescence microscopy images.

The SubcellularDistribution pipeline is written in Python and implemented using Jupyter notebooks and freely accessible on github (https://github.com/pearlryder/subcellular-distribution-pipeline). A detailed tutorial and a reference data set are provided to make the pipeline accessible for users who are less familiar with code-based image analysis. First, users provide single channel z-stack images of fluorescently labeled subcellular structures of interest (Fig. 1A). Analysis via the SubcellularDistribution pipeline is scalable and users can analyze as many subcellular structures as their imaging and post-processing setup can spectrally unmix. Next, images are segmented in 3D using batch processing with code adapted from the Allen Institute for Cell Science Cell Segmenter (Fig. 1B) (Chen et al., 2018 preprint). Briefly, image segmentation separates pixels into two groups: foreground pixels that are part of the structure of interest and background pixels that are not included in the analysis (Wiesmann et al., 2015). Next, groups of neighboring foreground pixels are automatically clustered into individual objects (Fig. 1C). For analysis, the SubcellularDistribution pipeline extracts the following features for each object: total pixel intensity in the original image, volume in pixels, the x, y, z coordinates of the centroid and the surface of the object, and the raw fluorescence image (Fig. 1D). These data are stored in a PostgreSQL relational database, which provides powerful features for parsing and analyzing the data (Fig. 1E). After object features are extracted, object coordinates are used to measure the pairwise closest distances between subcellular structures (closed arrow; Fig. 1F). Overlapping objects are assigned a distance of 0 µm (open arrows; Fig. 1F). The SubcellularDistribution pipeline includes an optional method to normalize single molecule fluorescence data (Fig. 1G). For single molecule normalization, users must predetermine the pixel volume of a single object. Finally, methods are included for visualization of the distribution of subcellular structures relative to each other (Fig. 1H). Taken together, the SubcellularDistribution pipeline offers users an open-source platform to detect, measure, and visualize distances between subcellular objects.

To confirm that our image analysis pipeline was able to similarly detect the distance between subcellular structures compared to more established methods, we used our pipeline and Imaris software to analyze images of mRNA detected by smFISH and centrosomes labeled with GFP-Centrosomin in Drosophila embryos (GFP-Cnn; Lerit et al., 2015).

Centrosomes function as microtubule organizing centers and ensure error-free cell division and cellular organization in animal cells (Conduit et al., 2015). Centrosomes are essential for Drosophila embryogenesis, where thousands of centrosomes reside close to the embryonic surface amenable to a variety of imaging approaches. During the first 2 h of Drosophila embryo development, the syncytial nuclei undergo 13 rounds of rapid and synchronized abridged S-to-M nuclear division cycles prior to somatic cellularization at nuclear cycle (NC) 14 (Foe and Alberts, 1983). For simplicity, we will refer to M-phase embryos within metaphase as ‘metaphase’ and embryos captured pre-prophase as ‘interphase.’

Localized RNA has long been implicated as a potential regulator of the centrosome, yet remains a relatively underexplored phenomenon (Marshall and Rosenbaum, 2000; Ryder and Lerit, 2018). Work from our lab and others recently showed that centrocortin (cen) mRNA forms local enrichments near centrosomes, unlike the metabolic enzyme gapdh (Fig. 2A,B) (Bergalet et al., 2020; Lécuyer et al., 2007; Ryder et al., 2020 preprint).

To compare the SubcellularDistribution pipeline with Imaris, we analyzed images of cen and gapdh mRNAs from Drosophila NC 12 embryos. In Imaris, we segmented images of centrosomes and RNA using the Surfaces tool and measured distances using the Distance Transform tool (Fig. 2C). In parallel, we compared these reference results to measurements obtained with the SubcellularDistribution pipeline (Fig. 2D). Both methods demonstrated that cen mRNA is more abundant near centrosomes in interphase and metaphase embryos than gapdh (Fig. 2E,F). To compare results from both platforms, distance measurements were plotted by calculating the cumulative percentage of total RNA that localized up to 5 µm from a centrosome (Fig. 2E,F). We found that the mean distance (dark line) and standard deviation (lighter shading) of cen and gapdh mRNAs derived from the two methods overlapped substantially, demonstrating that these approaches generate comparable results (Fig. 2E,F). Using a paired t-test to statistically compare the percent of RNA residing within 1 µm of a centrosome confirmed equivalent levels of cen or gapdh RNA (no significant difference; P>0.05, Fig. 2G,H). We conclude that the SubcellularDistribution pipeline generates object-to-object measurements comparable to Imaris, with the advantages of being free and readily customizable. Imaris also offers a batch mode to reduce active user-time, although we found that optimal segmentation was obtained by adjusting segmentation thresholds manually for each image. In contrast, image segmentation is run in batch in the SubcellularDistribution pipeline, reducing processing time and increasing reproducibility. For reference, we processed 24 images using Imaris for the analysis presented in Fig. 2, requiring approximately 6 h of active user-time. By contrast, the same images were processed with less than 1 h of active input using the SubcellularDistribution pipeline. Actual processing times will vary depending upon the subcellular surface area and computational setup. Finally, unlike Imaris, the SubcellularDistribution pipeline includes data visualization tools, further reducing data processing time.

Our pipeline operates via batch processing of images, which facilitates processing of large imaging datasets. We took advantage of this feature to process 81 images to investigate the distribution of another model transcript, small ovary (sov) mRNA, relative to centrosomes across multiple embryonic NCs. This analysis required less than 1 h of active user-time.

Sov facilitates heterochromatin stabilization and is required for embryonic viability (Benner et al., 2019; Jankovics et al., 2018). Although Sov protein resides within the nucleus (Benner et al., 2019), sov mRNA localizes near centrosomes (Lécuyer et al., 2007; Ryder et al., 2020 preprint). However, the patterns of sov mRNA localization across different syncytial blastoderm NCs have not been closely examined. Within interphase NC 10–14 embryos, gapdh mRNA remained dispersed throughout the cytoplasm (Fig. 3A–C). Similarly, in NC 10 embryos, sov smFISH also appeared predominantly cytoplasmic (Fig. 3D). By NC 12, however, more sov mRNA overlapped with centrosomes (Fig. 3E). Further enrichment of sov was observed at centrosomes in NC 14 embryos (Fig. 3F). Analysis using the SubcellularDistribution pipeline revealed significantly more sov overlapping with the centrosome relative to gapdh in interphase NC 10–14 embryos (Fig. 3G). In NC 12 and NC 14 embryos, sov was also significantly enriched in higher-order RNA granules, defined as RNA objects estimated to contain greater than four individual mRNA molecules (Fig. 3H) (Little et al., 2015). Within NC 14 embryos, 28.3±11.3% of sov mRNA was enriched at centrosomes (P<0.0001; Fig. 3G), and nearly 20% of sov was contained within pericentrosomal granules (P<0.0001; Fig. 3H). These findings reveal that the localization of sov mRNA to centrosomes is a developmentally regulated process and may be correlated with RNA granule formation. We note peak sov mRNA enrichment at centrosomes during NC 14 coincides with the onset of heterochromatin formation, hinting that aspects of sov mRNA localization may contribute to local Sov functions (Rudolph et al., 2007; Yuan and O'Farrell, 2016). Collectively, these data demonstrate that the SubcellularDistribution pipeline facilitates investigation of the relationship between subcellular structures across scalable datasets, such as multiple developmental stages or biological conditions.

Having established the utility of the SubcellularDistribution pipeline for measuring the distance between mRNAs and centrosomes, we next assayed its ability to detect other organelles, such as the nucleus. We examined the distribution of Bsg25D/ninein (nin) mRNA with smFISH. Nin is a pericentrosomal protein (Kowanda et al., 2016; Zheng et al., 2016) and nin mRNA localizes near centrosomes in syncytial embryos (Lécuyer et al., 2007). In NC 11 and NC 12 embryos, nin mRNA appeared evenly dispersed within the cytosol, similar to gapdh (Fig. 4A,B show representative images from NC 11 embryos; N=11 and N=12 embryos labeled for gapdh and nin mRNAs, respectively). Unexpectedly, we noted a marked enrichment of nin mRNA localized near nuclei in NC 13 and NC 14 embryos (Fig. 4C shows a representative image from an NC 14 embryo; N=12), while gapdh mRNA remained dispersed at this stage (Fig. 4D shows a representative image from an NC 14 embryo; N=11). We used the SubcellularDistribution pipeline to measure the distances between nin or gapdh mRNAs and nuclei using pooled datasets comprising images of early (NC 11 and 12) or late (NC 13 and 14) stage syncytial embryos. While NC 11–12 embryos showed no significant difference between nin and gapdh distributions, NC 13–14 embryos showed nin enriched near nuclei (Fig. 4E). These differences were readily detected within 1 µm of a nucleus (Fig. 4E′). We examined the relative levels of RNA residing 0 µm from a nucleus and found that 26.3±3.9% of nin mRNA overlaps with a nucleus, as compared to 15.5±2.6% for gapdh mRNA (P<0.0001 by unpaired t-test). Our data uncover an intriguing and previously unrecognized localization of nin mRNA within Drosophila embryos and reveal that nin mRNA redistributes to the perinuclear region during late-stage syncytial development. While the biological significance of nin mRNA localization to nuclei is currently unknown, Nin protein plays a key role in regulating nuclear position in Drosophila muscle cells (Rosen et al., 2019). Whether local nin mRNA resides near nuclei within muscle cells or otherwise contributes to Nin function warrants further study. Nevertheless, these findings highlight the utility of our pipeline to detect unique localization patterns between different subcellular structures. In principle, the relationship between any two or more fluorescently labelled subcellular structures may be interrogated using the SubcellularDistribution pipeline.

The SubcellularDistribution pipeline is an open-source and freely available software designed to analyze the distribution of subcellular structures from multichannel fluorescence microscopy images. This pipeline is designed to comprehensively guide the user through the complete process of preparing data for publication, from image segmentation and data processing to visualization. As an open-source tool, users can adapt the SubcellularDistribution pipeline to their own needs. In principle, the pipeline could be used to assess the distribution of a subcellular structure relative to multiple other subcellular structures. For example, organelle–organelle contacts could be analyzed using this tool. In addition, while we have built the pipeline to focus on the distribution of fluorescence intensity between two structures, the underlying Python framework is highly adaptable. The coding savvy user could investigate other object features, such as volume or shape, relative to their distance from a subcellular structure. The pipeline also enables biologists to learn basic coding skills in Python and SQL through the reference data set and tutorial. These skills are powerful and of high value, especially for trainees approaching the job market. Ultimately, the SubcellularDistribution pipeline is a new tool to promote biological discovery by providing a framework to rigorously test the relationship between subcellular structures.

P_BAC-GFP-Cnn flies express cnn tagged at the N-terminus with GFP under endogenous regulatory elements (Lerit et al., 2015). Flies were fed cornmeal molasses medium and maintained at 25°C in a light and temperature-controlled incubator.

Aged embryos (0.5–3 h) were collected on grape agar plates, and prepared for smFISH as described in Ryder et al. (2020 preprint). Briefly, embryos were fixed in a 1:4 solution of 4% paraformaldehyde:heptane for 20 min, devitellinized in methanol, and stored at −20°C or rehydrated using a stepwise series of methanol:PBST [PBS with 0.1% Tween-20), washed in wash buffer (WB: 10% formamide in 2× SSC supplemented with 0.1% Tween and 2 µg/ml nuclease-free BSA (VWR; #0332-25G)], and incubated in hybridization buffer (HB: 100 mg/ml dextran sulfate with 10% formamide in 2× SSC supplemented with 0.1% Tween, 2 µg/ml BSA, and 10 mM ribonucleoside vanadyl complex (RVC; New England Biolabs #S1402S) for 10–20 min at 37°C. Embryos were incubated overnight with Stellaris smFISH probes conjugated with either Quasar 570 or Quasar 670 dyes (Table S1) diluted 1:50 in HB, washed extensively in WB warmed to 37°C, stained with DAPI at 10 ng/ml (Thermo Fisher Scientific) in WB for 1 h, washed with PBST, and then mounted in Vectashield (Vector Laboratories; H-1000). Slides were stored at 4°C and imaged within 1 week.

Images were acquired on a Nikon Ti-E system fitted with a Yokogawa CSU-X1 spinning disk head, Hamamatsu Orca Flash 4.0 v2 digital CMOS camera, Perfect Focus system, and a Nikon LU-N4 solid state laser launch (15 mW 405, 488, 561, and 647 nm) using a 100×1.49 NA Apo TIRF oil-immersion objective. This microscope was controlled through Nikon Elements AR software on a 64-bit HP Z440 workstation.

The use of a Docker container system to run the SubcellularDistribution pipeline facilitates the installation of the necessary software packages and avoids potential package conflicts that may arise during Python installations. Software may be accessed here: (https://hub.docker.com/r/pearlryder/subcellular-distribution-pipeline). Users are provided with detailed instructions to install Docker and download the SubcellularDistribution Pipeline: (https://github.com/pearlryder/subcellular-distribution-pipeline). A preconfigured Docker image includes the necessary software packages to run the SubcellularDistribution pipeline, including: Psycopg 2, PostgreSQL (version 11.2), Seaborn (Waskom et al., 2020), Matplotlib (Hunter, 2007), NumPy (van der Walt et al., 2011), SciPy (Virtanen et al., 2020), and pandas (McKinney, 2010). A sample data set is provided to users on FigShare to learn the SubcellularDistribution pipeline: (https://figshare.com/projects/SubcellularDistribution_pipeline/86732).

Image segmentation is a binary classification of image pixels as either a component of the structure of interest or background. Our pipeline relies upon 3D-image segmentation to preserve spatial relationships captured by 3D imaging. Users may choose to segment their images using the software of their choice, including CellProfiler or Imaris (Carpenter et al., 2006). We provide instructions for segmenting images using the Allen Institute Cell Segmenter, a free and open-source software toolkit for 3D segmentation of microscopy images (Chen et al., 2018 preprint). We provide example Jupyter notebooks that are optimized for segmentation of smFISH and centrosomes. Briefly, this method prepares images for object detection by normalizing image intensities, removing background, and smoothing the images using 3D filters. Objects are identified using the Allen Institute's 3D spot filter. The watershed algorithm is applied to separate touching objects and a minimum volume threshold is applied.

Once images are segmented, Jupyter notebooks are used to identify individual objects and extract data about each of these objects. Objects are identified in segmented images using the label method from the SciPy ndimage package (Virtanen et al., 2020). This method identifies objects as connected components where adjacent pixels share the same values. The following object features are then extracted: total pixel intensity in the original image, volume in pixels, the x, y, z coordinates of the object centroid and surface, and the raw fluorescence image. These features are saved in a PostgreSQL database. Distances are measured between user-specified pairs of subcellular structures, referred to here as Structure 1 and Structure 2. The pipeline first estimates distances between objects by measuring distances between the centroid coordinates of the objects. This procedure allows for a quick estimate of the closest Structure 2 object for each Structure 1 object. The distances between surface coordinates are then measured between each Structure 1 object and the three Structure 2 objects that were closest by the centroid measurements. If users are measuring distances between densely packed structures, they can increase the number of objects that are measured. This approach decreases processing time. Parallel processing is included as an option to reduce processing time.

The SubcellularDistribution pipeline identifies single molecule objects using volume-based thresholding. For example, to normalize our smFISH data, we empirically determined that single molecules of RNA imaged with our optical system contain between 20 and 100 pixels. The pipeline calculates the average integrated fluorescence intensity of objects within these parameters, which is the average fluorescence intensity of a single molecule of RNA. To estimate the number of molecules of RNA per object, we divide each object's integrated fluorescence intensity by the average fluorescence intensity of a single molecule of RNA. This approach is adapted from Mueller et al. (2013) and Little et al. (2015).

The pipeline includes code to calculate the distribution profile of a subcellular structure relative to the distance from another subcellular structure. This calculation can be performed as fractions of Structure 1 at each distance from Structure 2 or as a cumulative distribution, where the percentage of Structure 1 that localizes within specified distances of Structure 2 is calculated for each image. For cumulative distributions, users specify a step size that determines the interval for these calculations (e.g. if users specify at step size of 0.05 μm, then the pipeline calculates the percentage of RNA localized at 0, 0.05, 0.1 μm, etc.). Users can also choose an upper distance threshold from a target object. These calculations can be saved as .csv files for plotting and/or statistical analysis. The pipeline includes code demonstrating how to plot these data using the Seaborn library (Waskom et al., 2020). Users may choose to analyze entire images or specific regions of interest, as detailed in the SubcellularDistribution pipeline github documentation.

Statistical analysis was performed using GraphPad Prism software (version 8.4.3). To calculate significance, the distribution normality was first assessed with a D'Agnostino and Pearson normality test. Data were then analyzed by Student's two-tailed t-test or the appropriate nonparametric tests and are displayed as mean±s.d.

Data tables may be exported from the Postgres database as .csv files. Instructions are also provided for saving database backup files.

We thank Jovan Brockett for testing software and members of the Lerit laboratory and Craig Durkin for constructive feedback during development of the SubcellularDistribution software.