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A high-resolution phenotypic screen identifies regulators of CNS axon diameter in zebrafish [1]

['Maria A. Eichel-Vogel', 'Institute For Neuroscience', 'Cardiovascular Research', 'University Of Edinburgh', 'Edinburgh', 'United Kingdom', 'Simons Initiative For The Developing Brain', 'Ms Society Edinburgh Centre For Multiple Sclerosis Research', 'Daniel Y. H. Soong', 'Zebrafish Imaging']

Date: 2026-07 

The molecular mechanisms underpinning neuronal morphogenesis remain to be fully understood. A powerful step in uncovering the molecular basis of biological processes is the execution of discovery screens in model systems. Here, we employed zebrafish to identify molecules that regulate axon diameter in the central nervous system. Axon diameter can vary by >100-fold, with larger axons exhibiting faster conduction velocity. Axon diameter influences myelination, can be dynamically regulated, and is altered in disease. However, gaining insights into axon diameter has been challenging due to the small size and complexity of axons in vivo. Therefore, we developed an automated high-resolution live imaging and analysis screening platform to identify pharmacological modulators of the diameter of the large Mauthner axon in zebrafish. We screened 880 compounds and identified 33 hits. Validating this pipeline, we confirmed that compounds affecting beta-2 adrenoceptor and dopamine signaling increase axon diameter. This represents the first subcellular in vivo imaging-based screen to detect changes in axon diameter, providing entry points to study the biology of this key feature of neuronal morphology.

Funding: This work was supported by Wellcome Trust Senior Research Fellowship (214244/Z/18/Z) to D.A.L. D.A.L. and J.M.B. received salary support through this award. and a UKRI Frontier Research grant, administered through the Engineering and Physical Sciences Research Council (EPSRC), (EP/Z533890/1) to D.A.L. D.A.L. and M.L.A.I. received salary support through this award. M.A.E.V. is a recipient of a Walter-Benjamin Fellowship by the Deutsche Forschungsgemeinschaft (DFG grant 493410640 to M.A.E.V.) and a UKRI postdoctoral fellowship, administered through the Engineering and Physical Sciences Research Council (EPSRC) (EP/Y029577/1 to M.A.E.V.). M.A.E.V. received salary support through these awards. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. URLs for each funder: Wellcome Trust: https://wellcome.org/ UKRI (EPSRC) https://www.ukri.org/councils/epsrc/ DFG: https://www.dfg.de/en .

To perform a screen for axon diameter, we reasoned that it would be critical to use an imaging platform that allows us to resolve even subtle changes to the diameter of individual axons in vivo. We also sought to automate this process to increase scalability for screening. Our previous studies have shown that the Mauthner axon is well suited to the study of axon diameter in vivo, due to it being distinctly identifiable and exhibiting robust and early axon diameter growth during the development and functional maturation of a well-characterized neural circuit. Here, we describe the development of an automated imaging and machine learning (ML)-based analysis pipeline to assess changes to Mauthner axon diameter in the zebrafish, which grows several microns in diameter over its first few days of development [ 20 ]. With this, we executed a large chemical-compound-based discovery screen and validated hit compounds that increased axon diameter. Our work highlights the potential to screen for subtle changes to cell morphology in vivo, which could also be employed to assess other poorly understood features of neuronal structure. Further, we provide the findings from our screen as a resource for the community to accelerate investigation into the biology of axon diameter.

Recently, we established zebrafish as a model to study axon diameter and used transgenic reporters to observe axon diameter growth in vivo over time [ 20 , 44 ]. Following a genetic screen for new genes required for myelinated axon formation, we identified the nuclear transport receptor ipo13 as being essential for the growth in diameter of very large axons [ 20 ]. This capacity motivated us to develop a discovery platform that aimed to specifically identify novel regulators of axon diameter.

Across the vertebrate nervous system, for example, the diameter of axons can vary by >100-fold, from ~0.1 µm to tens of microns, equating to ~10,000-fold difference in cross-sectional area occupied by distinct axons [ 10 ]. The ~100-fold difference in axonal diameter is accompanied by a roughly 100-fold difference in the speed of action potential conduction along axons, with larger axons conducting proportionally faster [ 11 – 13 ]. In addition to differences between axons, diameter can vary along individual axons, including between branches, and over time. Axon diameter can be dynamically regulated via cell-cell interactions [ 14 ], including signals from myelinating glia cells, particularly in the PNS [ 15 – 20 ] and neuronal activity in healthy conditions [ 21 – 28 ]. Further, axon diameters are altered in a range of diseases and injuries [ 29 – 36 ]. Our understanding of the molecular mechanisms underpinning axon diameter growth, modulation, or changes in disease remain largely unclear, with insights primarily relating to the relationship between the axonal cytoskeleton and axon size [ 15 , 37 – 41 ]. For example, positive correlations between axon diameter and neurofilament density and phosphorylation, particularly for larger myelinated axons have been observed [ 15 , 37 , 42 ]. In smaller, unmyelinated axons, microtubule organization supports axon diameter growth [ 10 , 37 ]. More recently, it has been reported that the neuronal actomyosin network, particularly alpha-adducin, an actin capping protein, controls the diameter growth of axonal actin rings, thereby maintaining axonal diameters and integrity [ 40 , 41 ] (reviewed by [ 43 ]). Most of these insights into the biology of axon diameter have been driven by the analysis of fixed tissue preparations by high-resolution imaging approaches such as electron microscopy and super-resolution imaging. However, these approaches are not yet experimentally scalable and so discovery-based screens applied to axon diameter have not yet been carried out in model systems.

Our understanding of the mechanisms that determine and regulate the complexity of cellular morphology and function in vivo remain to be fully understood. In particular, our understanding of what controls the enormous diversity and complexity of neurons in the central nervous system of vertebrates is incomplete. Major insights into the growth and connectivity of neurons have derived from phenotypic discovery screens carried out in model organisms, including Drosophila, C elegans and zebrafish, that have identified factors required for axonal pathfinding, innervation and synapse formation [ 1 – 9 ]. However, additional features of neuronal morphology have remained relatively intractable. For example, our understanding of what controls the axonal shape, particularly the diameter of axons, is relatively limited. This is largely due to difficulties in visualizing the diversity of and dynamic changes to axon diameter in intact systems or circuits.

Results

Automated high-resolution imaging of axon diameter in vivo using zebrafish larvae To investigate the molecular regulation of axon diameter in the CNS, we chose the Mauthner neurons and their axons as a model. The Mauthner neurons are a pair of reticulospinal CNS neurons located in the hindbrain and critical for mediating fast escape responses that project long large-diameter axons along the length of the spinal cord to drive motor outputs [45–47]. Our previous assessments of the Mauthner axon diameter employed high-resolution confocal live imaging of fluorescent reporters, which indicated the axon grows from ~1 to ~5 µm in diameter between 2- and 5-days post-fertilization (dpf) [20,44]. Such measurements of diameter were carried out either manually or using semi-automated analyses, unsuitable to scalable measurements. Therefore, to implement methods suited to a scalable discovery screen, we aimed to automate both the imaging and analysis of Mauthner axon diameter, balancing high-resolution imaging and high-throughput. We previously employed automated live imaging of zebrafish expressing fluorescent reporters to screen for compounds that influenced myelinating glial cell number [48]. To do so, we employed a spinning disk confocal microscope (SDCM) for image acquisition, coupled to the vertebrate automated screening technology (VAST) platform for the automated delivery of zebrafish larvae from a 96-well plate to the microscope for imaging. The VAST platform delivers zebrafish to a thin-walled glass capillary (600–750 µm bore), in which they are stabilized and automatically oriented to visualize the animal from a specific orientation. The system then hands control to the SDCM to acquire images of the animal within the glass capillary. Here, we used the transgenic reporter Tg(hspGFF62A:Gal4); Tg(UAS:GFP) [8,49] to image growth in the diameter of the Mauthner axon. Due to Mauthner axons being close to midline axis, we needed to use a long-working-distance objective to image through the tissue overlying the axons and the glass capillary. We tested both 10× and 20× objectives and acquisition protocols that imaged either the entire animal (entire length of the Mauthner axon, Fig 1A) or just one defined region (mid-way along axonal projection, Fig 1B) (see Methods and comparison of parameters shown in Fig 1E). This indicated that imaging one z-stack at 20× magnification provided the optimal parameters that balanced acquisition resolution and throughput necessary for a screen. PNG larger image

TIFF original image Download: Fig 1. Axon diameter changes can be visualized and assessed using an automated screening system. (A) Maximum intensity projection of images of Tg(hspGFF62A:Gal4); Tg(UAS:GFP) zebrafish following tiled merging of 6 z-stacks. Z-stacks were acquired from lateral view with a 10×/0.5NA objective using VAST-SDCM system. Scale bar = 500 µm). (B) Maximum intensity projection of 1 tile z-stack at approximately somite 15–16 of Tg(hspGFF62A:Gal4); Tg(UAS:GFP) fish imaged with a 10×/0.5NA (top) or a 20×/1NA objective (bottom). Scale bars = 50 µm B22) Zoom in of B shows improved subcellular resolution at 20× compared to 10×. Scale bars = 25 µm. (C) Script-based semi-automatic quantifications of Mauthner axon diameter show that we can measure axon diameter changes using the 20×/1NA objective and imaging parameters in B. When applying the mTor inhibitor Rapamycin we detect a significant decrease in Mauthner axon diameter (n = 9, ***p < 0.0001). (D) Quantifications of Mauthner axon diameter of larvae kept in either E3 embryo medium (controls) or in 0.5% (left panel) or 1% DMSO (right panel). When applying 1% DMSO we detect a significant decrease in Mauthner axon diameter (n > 7, p = 0.0034). We detect no difference when applying 0.5% DMSO (n = 12, p = 0.94). (E) Imaging resolution and times for 2 × 2 binning levels using either 10× or 20× objective and imaging Tg(hspGFF62A:Gal4); Tg(UAS:GFP) reporter line at 300 ms exposure at the VAST-SDCM. The data underlying this figure can be found in S1 Data. https://doi.org/10.1371/journal.pbio.3003895.g001 Next, we wanted to ensure that images acquired in this way allowed us to detect experimentally induced changes in axon diameter. We performed manipulations after the Mauthner axon has completed its outgrowth, reasoning that this would also limit identifying false positive candidates that influenced axon diameter by affecting development or earlier stages of neuronal maturation. We first assessed whether we could detect changes in axon diameter induced by manipulating the mTor pathway, which has been shown to affect axon diameter [50,51]. To do so, we applied the mTor inhibitor Rapamycin at 50 µM from 3 to 5 dpf, during active diameter growth, and were indeed able to detect a decrease in Mauthner axon diameter of around 10% upon Rapamycin treatment (with n = 9) (Fig 1C). It is important to note that while DMSO at 0.5% does not impact axon diameter growth, 1% DMSO significantly decreases Mauthner axon diameter compared to larvae kept in E3 embryo medium (controls) (Fig 1D). To screen ~1,000 compounds for their potential to alter axon diameter at this scale, we anticipated needing to acquire ~10,000 z-stacks from 10,000 animals (n ~ 10), including controls and occasional acquisition failures. This scale of analysis motivated us to develop an entirely automated image analysis pipeline.

Creating an automated image analysis pipeline to measure axon diameter changes at subcellular resolution Having established imaging parameters that can detect changes to axon diameter at 5 dpf, we next developed a fully automate pipeline to measure of axon diameter. The transgenic line Tg(hspGFF62A:Gal4); Tg(UAS:GFP) that we used to label the Mauthner axon also labels neurons in the posterior lateral line nerve and, on occasion exhibits ectopic fluorescence in other cells (e.g., muscle and skin), as is common for transgenic zebrafish with Gal4:UAS-driven expression (Figs 1A and 2A). Variation caused by ectopic fluorescence expression, as well as autofluorescence, skin pigmentation, and biological batch/clutch variations, lead to an extensive range of inter-image variability and quality. Furthermore, although the VAST system orients the zebrafish in a lateral position with great fidelity, it does not ensure that the midline of the animal runs parallel to the midline of the capillary tubing. Therefore, the trajectory of the Mauthner axon is not always constrained to a predictable number of z-sections in acquired stacks. For these collective reasons, it was not possible to simply convert 3D z-stacks into 2D maximum intensity projections for threshold-segmented analysis. Instead, we employed ML to segment objects of interest in manner that is more agnostic to intensity or position (Fig 2). PNG larger image

TIFF original image Download: Fig 2. Creating an automated image analysis pipeline to measure axon diameter changes: Stepwise object detection and processing. Front and top 3D views of z-stacks rendered in Arivis Vision4D showing: (A) Overly contrasted grayscale look-up-table to highlight problematic skin reflection/autofluorescence (solid arrow), and ectopic muscle expression of GFP (hollow arrows). (B) Low-resolution ML model trained to exclude skin and muscle leaving candidate axonal objects (green). (C) Object filtering to exclude small objects (green), leaving target Mauthner axons (orange). (D) High-resolution ML model trained to tightly segment clear and reliably measure candidate axons (yellow). (E) Object size filtering followed by a clustering python script determines objects most likely to be the Mauthner axon closest to the objective (green), other objects (yellow) are discarded. (F) Final segmented Mauthner axon (green) used for measurement. (G) Mauthner axon object slices are exported and z-projected to create a 2D planar mask. (H) 2D planar mask is polygonised and the polygonal skeleton used to take multiple skeletal width measurements across the length of the detected axon. https://doi.org/10.1371/journal.pbio.3003895.g002 Many image analysis platforms, both open-source and commercial, now implement ML or Artificial Intelligence (AI)-based training and segmentation. Here, we employed multiple levels of random forest pixel classification using Arivis Vision4D to generate an automated pipeline for consistent batch measurement of Mauthner axon diameter in live zebrafish. Single-field imaging of the spinal cord at somite 15–16 was conducted as described above (see also Methods). Selected confocal stacks from multiple runs, encompassing multiple batches, were imported into Vision4D to create a training data set. Each slice of each image was heavily blurred (gaussian, 146 µm radius, alternative software ImageJ) to create an additional low-resolution denoised channel. This channel and the original were used to train a low-resolution random forest pixel classifier model (see Methods) that would detect, very roughly, the Mauthner axons and exclude most artifacts or unwanted signal (highlighted in Fig 2A and 2B, muscles (open arrows), autofluorescence of the skin (solid arrows)). Next, a high-resolution model (see Methods) was trained using only the original channel raw data. Thus, objects outwith the defined size, shape and intensity range were filtered out (Fig 2B–2D, steps 1–9 in workflow diagram Fig 3A, and S1 Video). The imaging, and therefore the segmentation quality, could vary depending on the positioning of the fish and proximity to the objective lens. To account for this, we divided the axons into “top” and “bottom” groups based on their relative z-position, enabling separate analysis of the better-resolved (top) axon. To sort the axon objects into those nearest the objective lens (top) and those furthest away (bottom), relative or relational object filtering was required (e.g., “highest” of two objects with “similar” × centroid values, or group objects with “similar” z centroid). Therefore, a python script was created to cluster objects in the z-dimension and sort the clustered objects into “top” and “bottom” axons (imaged lateral-side-up) (Fig 2E and 2F). This step works partially outside of the image-handling environment and so is represented as an additional process (Fig 3A, dotted arrows). PNG larger image

TIFF original image Download: Fig 3. Manual assessment confirms reliability of the machine learning-based quantification of Mauthner axon diameter. (A) Workflow diagram illustrates the step-by-step process to create our fully automated image analysis pipeline; the tools used in our study are in bold and alternative suggestions and open-source tools are indicated in italic. All scripts and pipelines can be accessed via Edinburgh Datashare (see Methods). (B) Representative thermal look-up table (LUT)-thresholded image of a selected region of a Mauthner axon with an overlaid random measurement grid (yellow, 500 µm2 spacing). Manual tracing was performed on a minimum of five grid tiles per sample to calculate mean axon diameter, as described in Methods. (C) Representative image of the mask of the complete Mauthner axon in B generated by our machine learning pipeline (ML). Pink box indicates the region shown in B. (D) Direct per-sample comparison of Control Mauthner axons, assessed by our automated image analysis pipeline (ML) and manually by two independent assessors (Manual 1 and 2). Each sample is color-matched indicating that Mauthner axon diameter values differ across all 3 measures. (n = 5). (E) Changes in Mauthner axon diameter for Nefmatg/+ were normalized to the respective, assessment-matched controls and calculated in %. Both ML pipeline and manual assessments detect a comparable, statistically significant decrease in Mauthner axon diameter for Nefmatg/+ compared to controls. One-way ANOVA with multiple comparison test was used to assess statistical significance. Error bars represent mean with SD. P-values are presented as * p < 0.05, ** p < 0.01, *** p < 0.001. Scale bar = 20 µm. The data underlying this figure can be found in S1 Data. https://doi.org/10.1371/journal.pbio.3003895.g003 To obtain an average width measurement for each axon, per-slice binary masks for the top Mauthner axon were exported, projected in Z, and measured in another software package (Definiens DeveloperXD). An example of the process can be seen in Fig 2G and 2H. The workflow diagram (Fig 3A) shows the steps used to create our fully automated image analysis pipeline, states the tools employed in this study (bold text), and suggests alternative or open-source tools that can be used to achieve analogous goals (italic text). A collection of scripts and pipelines for each step are deposited at Edinburgh Datashare (https://doi.org/10.7488/ds/8111). To test the performance of the automated image analysis pipeline, we used an available transgenic line [52] (here referred to as nefmatg/+) that we found to exhibit smaller Mauthner axon diameters and imaged it using our standardized VAST-SDCM settings. We then compared axon diameter measurements obtained from the automated analysis pipeline (ML) with those from fully manual assessments done by two different experimenters (Manual 1 and 2) (see Methods, Fig 3B and 3E). Direct, per-sample comparison of control images showed that Mauthner axon diameter measurements differed between the ML approach and the manual assessors, and between the two manual assessors (Fig 3D). In addition, we observed that manual assessment results in higher variability, together highlighting the importance of an unbiased, automated measurement approach. Notably, independent of ML or manual assessment, we detected a significant ~20% decrease in Mauthner axon diameter in the reporter line compared to controls (Fig 3E). Taken together, we reasoned that our imaging and analysis pipeline is sensitive enough to detect changes in Mauthner axon diameter.

Chemical screen reveals compounds that change Mauthner axon diameter After establishing our imaging and analysis pipeline, we next wanted to carry out the screen to identify factors that changed axon diameter. We used the LOPAC1280 library because of its well-described compounds, with annotated targets that represent diverse pathways. In addition, it includes a particularly high portion of compounds related to neurotransmission- and ion channel-function. Because it was previously shown that axon diameter can change upon neuronal activity ex vivo [24], we reasoned that this library might enrich for compounds that impact axon diameter growth in vivo. For zebrafish screens, compounds in the LOPAC library have commonly been used at around 10 µM to prevent severe impacts on health [53,54]. Since we aimed to treat our larvae at slightly later stages (3–5 dpf), we initially tested 30 randomly chosen drugs at 2, 10, 50, and 100 µM and assessed the survival and health of fish. At 10 µM, we observed little impact on health and survival of larvae (~1% mortality) and thus selected 10 µM as the primary concentration for each compound. Compounds that did cause larval death at 10 µM were rescreened at 2 µM in the primary screen. To maximize our throughput and test more compounds per 96-well plate, we placed 3 fish/well which allowed testing of 30 drugs (3 wells per drug for total of 9 animals per compound plus 6 wells of controls: each 96-well plate of 3 animals per well was imaged in ~5 hours. To accommodate for clutch-specific differences in axon diameter and for any changes that may occur to diameter over the imaging period, we included 3 wells with 3 fish/well treated with 0.5% DMSO as clutch-specific controls, spread out across the 96-well plate. In the screen, animals were treated with compounds from 3 to 5 dpf, imaged at 5 dpf and assessed using our automated analysis pipeline (Fig 4A). To assess how test compounds influence axon diameter, we normalized compound effects on axon diameter relative to controls and calculated a z-score to rank how compounds differed from controls. We did so by comparing axon diameter measurements for each drug-treated sample to those of the corresponding DMSO-treated clutch and plate-specific control (see Methods for calculations). This standardization is common practice in high-throughput drug screening approaches because it accounts for variability across wells, plates and batches and allows ranking of the impact the identified hits have [55]. Given that we image at subcellular resolution and aim to detect changes as small as 10% in an axon as big as the Mauthner axon, we set a threshold z-score >+1.2 or <−1.2 to define hit compounds in the primary screen. To minimize technical, imaging, or analysis-related errors we manually inspected the z-stacks and masks for each candidate hit as well as their respective controls. In some cases, imaging errors (e.g., drift of fish in the z-axis) led to incorrect detection of the Mauthner axon. Therefore, we excluded any data points without accurate axon identification and mask-based measurement from our analysis (For a detailed description of each step, see Methods). PNG larger image

TIFF original image Download: Fig 4. Phenotypic screen identifies compounds that change Mauthner axon diameter. (A) Schematic illustrating the workflow of the screening pipeline. (B) In the primary screen 880 drug compounds were tested at either 2 or 10 µM for their potential to alter Mauthner axon diameter and approximately 5% were initially identified as hit compounds. A further 3% resulted in death at both dilutions and 17.5% were excluded due to technical issues. (C) The z-Score was calculated by comparing each drug with its respective DMSO-treated clutch-specific control. A z-Score of >1.2 or <−1.2 was considered changed in diameter. Twenty-eight compounds decrease and 17 compounds increase Mauthner axon diameter. (D) Brightfield images show a healthy appearing control (top), a larva with smaller body dimensions (middle) and one with an unhealthy appearance (bottom). Scale bars = 1 mm. (E) Body dimensions (length and area) of each hit compound and their respective DMSO-treated clutch control were subsequently assessed from brightfield images. Changes in over 3% body area and 2.5% length were considered as exclusion criteria (pink). Toxic compounds are indicated in red. (F) Results of the primary screen displays hit compounds ranked by z-Score (X-axis) and statistical assessment using Student t test (Y-Axis) comparing each hit compound with its DMSO-treated clutch-specific control. Thirty-three candidate hit compounds are color coded by class of action (turquoise Ion channels, yellow Neurotransmitters; green iNos/eNos species and gray Miscellaneous). In total, 18 drug compounds decrease Mauthner axon diameter beyond z-Score threshold of −1.2 and 15 drug compounds increase Mauthner axon diameter above threshold of 1.2. See also S1 Table for description of compounds. The data underlying this figure can be found in S1 Data. Schematic in A Created in BioRender. Lyons, D. (2026) https://BioRender.com/puyi62e. https://doi.org/10.1371/journal.pbio.3003895.g004 In total, we tested 880 drugs for their ability to affect axon diameter. We found that 28/880 drugs (3.18%) were lethal at both 10 and 2 µM dilution and 154/880 treatments (17.5%) had to be excluded due to technical difficulties (low image quality, false detection of the Mauthner, n number <4). Of the remaining compounds, 653 (74.2%) did not meet the z-score threshold, while 45/880 compounds (approximately 5%) were classified as hit compounds (Fig 4B and 4C). To check for the impact each candidate hit might have on the health of the fish, we manually assessed all accompanying brightfield RGB images acquired by VAST, for any discoloration (sign of ill health) or deformations. We excluded 2 compounds because fish were discolored or their spine slightly deformed (Fig 4D). Next, we wanted to make sure that the remaining 43 hit compounds did not influence axon diameter secondary to impacting the overall size of the fish. To exclude candidate hits that primarily impact body dimensions, we measured body area and length of all animals treated with candidate hits and their respective controls. Determining relatively subtle changes to animal size (e.g., on the order of 10%, Fig 5A) by eye is very challenging so we applied further automated methods. This time, we trained a random forest pixel classifier to recognize whole fish larvae (Fig 5B). We measured body area and length using measurements built into DeveloperXD as described in Methods (Object Area, Length (bounding box)). To define exclusion criteria, we first assessed variation in whole-body area and fish length. We did so by pooling the values for area and length of DMSO-treated controls across various clutches and imaging rounds and calculating their mean values, and standard deviation. A single standard deviation from the mean length of all controls (3.93 mm) was 2.25% and of area (1.41 mm2) was 2.92%. Therefore, we considered changes exhibited by hits that were greater than a single standard deviation as likely affecting animal size and thus set a change of both 2.5% in length and 3% area as an exclusion threshold for altered body dimensions (see Methods; stippled lines Fig 4E). This resulted in the exclusion of 10 hit compounds (Fig 4E). Validating these exclusion criteria, we found that changes in body area and length were significantly correlated with axon diameter for the 10 excluded hits (R2 = 0.72 for area and R2 = 0.69 for length) (Fig 5C). In contrast, there was no correlation between axon diameter and body area or length for the remaining 33 candidate hits (R2 = 0.02 for area and R2 = 0.01 for length) (Fig 5D), suggesting alteration to axon diameter independent of body size. PNG larger image

TIFF original image Download: Fig 5. Assessment of exclusion criteria for body dimensions. (A) The two depicted brightfield images acquired on VAST-SDCM of DMSO-treated controls vary by 10% in area which we cannot manually assess by eye. Scale bars = 1 mm. (B) Body dimension (length and area) was calculated on brightfield images from VAST-SDCM by training a machine learning classifier in DeveloperXD. Exclusion criteria (3% area change and 2.5% length change) were estimated based on averaged variations across different DMSO-treated controls (See Methods for details). (C) Hit compounds that were excluded based on body size are shown in pink. For those, both area and length correlate with axon diameter (R2 = 0.72 area and R2 = 0.69 length). (D) Hit compounds without change in body dimensions are shown in black (right) and have no correlation between area or length and axon diameter (R2 = 0.02 area and R2 = 0.01 length). The data underlying this figure can be found in S1 Data. https://doi.org/10.1371/journal.pbio.3003895.g005 Among the 33 remaining hit compounds, 18 decreased Mauthner axon diameter, while 15 increased it (Fig 4F and S1 Table). Notably, 19 of the overall 33 hit compounds are linked to neurotransmission and ion channel function. This enrichment is consistent with the LOPAC library composition, in which ~60% are neurotransmission and ion channel-related compounds. In contrast, other compounds target nitric oxide species, lipid signaling, phosphorylation, cytoskeleton, and extracellular matrix. To complement the z-score ranking, we performed a Student t test comparing each hit compound exclusively to its respective DMSO-treated control, enabling standardized effect size assessment and to serve as guidance which compounds to prioritize for validation. This analysis identified 18 out of 33 compounds that statistically significantly alter axon diameter. S1 Table highlights all hit compounds, including their ranking metrics and action class. In summary, we performed the first in vivo discovery screen designed to identify compounds that modulate axon diameter. Out of 880 compounds tested, following rigorous data curation, we identified 33 compounds that influence Mauthner axon diameter growth. We provide our complete list of hit compounds to the neuroscience community as a resource to facilitate further investigations.

Validation of hit compounds reveals that beta-2-adrenoceptor agonists and dopamine antagonists increase Mauthner axon diameter growth Next, we wanted to validate hit compounds from our primary screen. We first assessed the action class of compounds and prioritized hit compounds that might influence axon diameter growth by modulating neuronal activity. In addition, we prioritized compounds according to (1) their z-score and effect size rank; (2) repeated appearance of their class of action amongst hits; (3) presence of both antagonist and agonist of the same class that introduce opposing effects on diameter; and (4) their ability to increase the diameter of the already large Mauthner axon. Based on these triage steps, we first focused on compounds linked to adrenoceptors (2 antagonists decreasing and 1 agonist increasing diameter) and dopamine signaling (2 antagonists increasing axon diameter). We ordered the same compounds or those that target the same pathway from different suppliers. To assess dose-dependent effects of the new compounds on Mauthner axon diameter, we performed serial dilutions ranging from 1 to 100 µM and manually assessed compound toxicity by eye to determine which concentrations induced developmental deformities or lethality of larvae. We excluded these concentrations from further analysis. When available, we re-tested the original LOPAC library compound at the concentration we used in our initial screen (10 or 2 µM). While the VAST-SDCM system and our imaging pipeline enabled us to perform the large-scale primary screen, our validation experiments did not require high-throughput imaging. To achieve higher resolution imaging, we used our previously established method for imaging axon diameter by confocal super-resolution imaging (Figs 1D, 6F, and 6F′). We imaged Mauthner axons of drug-treated and DMSO-treated control larvae and used a semi-automated script to measure axon diameter, given that our ML-driven image analysis was bespoke to images acquired on our VAST-SDCM platform. To account for clutch-specific variability we normalized axon diameter by calculating the change relative to DMSO-treated controls. PNG larger image

TIFF original image Download: Fig 6. Beta-2-adrenoceptor agonists and dopamine antagonists increase Mauthner axon diameter growth. Validation of selected hit compounds different vendors was performed at different concentrations (1–50 µM) and compared to control fish treated with 0.5%–1% DMSO. Fatal or health-impacting dilutions are excluded. The change of Mauthner axon diameter for each drug was normalized to the clutch-specific DMSO-treated control and calculated in %. If available, the original LOPAC1280 drug was tested at its original dilution (LP). (A) Beta-2 Adrenoceptor agonist Formoterol resulted in a significant increase in Mauthner axon diameter at 4 µM and when testing the original LP dilution at 10 µM. (B) A second beta-2 adrenoceptor agonist, Fenoterol hydrobromide shows an increase in Mauthner diameter at 1 and 4 µM. (C) Alpha Adrenoceptor antagonist Fenspiride hydrochloride did not show the initially detected decrease in Mauthner axon diameter, no significant change was detected. (D, E) Both Dopamine antagonist Domperidone and Eticlopride resulted in a significant increase in Mauthner axon diameter. (F–F′) Fish were imaged using 40 × objective at a Zeiss LSM880 with AiryScan FAST mode with 1.8 zoom for validation studies. Exemplified confocal images and zoom in (F′) visualize increase in Mauthner axon diameter when fish were treated with Dopamine antagonists Domperidone and Eticlopride. (G) Exemplary images for PLLn axons comparing control and Nefmatg/+ larvae. (H) PLLn axon diameter is significantly decreased in Nefmatg/+ transgenic line compared to control. (I, J) Both, Formoterol (I) and Domperidone (J) did not show a statistically significant change in PLL axon diameter but indicate a trend towards increased diameters for Formoterol. If applicable, individual clutches are shown in open vs. closed circles. N number indicated by individual data points in all graphs. One-way ANOVA followed by Dunnett`s multiple comparison test (A–E) or Student t test (H–J) was used to assess statistical significance. Multiple adjusted p-values are shown in each figure panel and are presented as * p < 0.05, ** p < 0.01, *** p < 0.001. Error bars represent mean with SD. Scale bars = 10 µm. The data underlying this figure can be found in S1 Data. https://doi.org/10.1371/journal.pbio.3003895.g006 Our validation tests revealed that the beta2-adrenoceptor agonist Formoterol increases the axon diameter of the Mauthner at 4 µM (~8%), with the original LOPAC1280 compound also showing a significant increase (~11%) in diameter at 10 µM (Fig 6A). To assess another beta-2 adrenoceptor agonist, we applied Fenoterol hydrobromide and detected a significant increase in axon diameter at 2 and 10 µM (~8% to 9%, Fig 6B). We did not detect changes in Mauthner axon diameter when applying the alpha-adrenoceptor antagonist Fenspiride hydrochloride, which decreased axon diameter in the initial screen (Fig 6C). We were not able to acquire the third adrenoceptor-related compound A315456 on the market. Next, we assessed the two hit compounds targeting Dopamine signaling: the dopamine antagonists Domperidone and Eticlopride. Both compounds significantly increased diameter between 6% and 10% in our validation studies (Fig 6D–6FFigure 6EFigure 6F′). Next, we wanted to know if these hit compounds influenced the diameter of other axons, particularly those of smaller diameter than that of the Mauthner neuron. The transgenic line Tg(hspGFF62A:Gal4) used throughout this study also labels neurons of the posterior lateral line (PLLn) nerve of the peripheral nervous system. To test whether we could detect changes in the diameter of PLLn axons, we used the Nefmatg/+ line and observed a significant ~22% decrease compared to controls (1.226 µm control and 1.027 µm Nefmatg/+, n > 15). When applying Formoterol or Domperidone, we did not detect a statistically significant difference in PLLn axon diameter (Fig 6I and 6J). Nonetheless, mean values for the Formoterol dataset were 0.921 µm for DMSO-treated controls and 0.997 µm for Formoterol-treated larvae. This mean difference of approx. 70 nm reflects an 8% percent increase, very close to the one observed for the Mauthner axon (8.9%; Fig 6A) suggesting a trend towards increased axon diameter with beta-2 adrenoceptor agonist Formoterol. See data source file for considerations for power calculations for the detection of subtle changes to axon diameter.

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[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003895

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