1. Developmental Biology
  2. Stem Cells and Regenerative Medicine
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3D visualization of macromolecule synthesis

  1. Timothy J Duerr
  2. Ester Comellas
  3. Eun Kyung Jeon
  4. Johanna E Farkas
  5. Marylou Joetzjer
  6. Julien Garnier
  7. Sandra J Shefelbine
  8. James R Monaghan  Is a corresponding author
  1. Department of Biology, Northeastern University, United States
  2. Department of Mathematics, Laboratori de Càlcul Numeric (LaCàN), Universitat Politècnica de Catalunya (UPC), Spain
  3. Department of Mechanical and Industrial Engineering, Northeastern University, United States
  4. University of Technology of Compiègne, France
  5. Department of Bioengineering, Northeastern University, United States
  6. Institute for Chemical Imaging of Living Systems, Northeastern University, United States
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Cite this article as: eLife 2020;9:e60354 doi: 10.7554/eLife.60354

Abstract

Measuring nascent macromolecular synthesis in vivo is key to understanding how cells and tissues progress through development and respond to external cues. Here we perform in vivo injection of alkyne- or azide-modified analogs of thymidine, uridine, methionine, and glucosamine to label nascent synthesis of DNA, RNA, protein, and glycosylation. Three-dimensional volumetric imaging of nascent macromolecule synthesis was performed in axolotl salamander tissue using whole-mount click chemistry-based fluorescent staining followed by light sheet fluorescent microscopy. We also developed an image processing pipeline for segmentation and classification of morphological regions of interest and individual cells, and we apply this pipeline to the regenerating humerus. We demonstrate our approach is sensitive to biological perturbations by measuring changes in DNA synthesis after limb denervation. This method provides a powerful means to quantitatively interrogate macromolecule synthesis in heterogenous tissues at the organ, cellular, and molecular levels of organization.

eLife digest

Cells often respond to changes in their environment by producing new molecules and building new cell components, such as proteins, which perform most tasks in the cell, or DNA and RNA, which carry genetic information. Complex tissues – such as limbs, which are made up of muscles, tendons, bones and cartilage – are difficult to see through, so studying when and where cells in these tissues produce different types of molecules is challenging. New approaches combining advanced three-dimensional microscopy and fluorescent labelling of molecules could provide a way to study these processes within whole animal tissues. One application for this is studying how salamanders regrow lost limbs.

When salamanders such as axolotls regrow a limb, some cells in the limb stump form a group called the blastema. The blastema contains cells that are specialized to different purposes. Each cell in the blastema produces many new proteins as well as new DNA and RNA molecules. Fluorescently labeling particular molecules and taking images of the regenerating limb at different times can help to reveal how these new molecules control and coordinate limb regrowth.

Duerr et al. developed a three-dimensional microscopy technique to study the production of new molecules in regenerating axolotl limbs. The method labeled molecules of different types with fluorescent markers. As a result, new proteins, RNA and DNA glowed under different colored lights. Duerr et al. used their method to show that nerve damage, which hinders limb regrowth in salamanders, reduces DNA production in the blastema.

There are many possible applications of this microscopy method. Since the technique allows the spatial arrangement of the cells and molecules studied to be preserved, it makes it possible to investigate which molecules each cell is making and how they interact across a tissue. Not only does the technique have the potential to reveal much more about limb regrowth at all stages, but the fluorescent markers used can also be easily adapted to many other applications.

Introduction

The measurement of nascent DNA, RNA, and protein synthesis in animals provides critical information on the state of cells (dividing, growing) in relation to their surrounding cells. Traditionally, radiolabeled or brominated nucleosides/amino acids are introduced to live animals, where they are incorporated into macromolecules during DNA synthesis (Sidman et al., 1959; Gratzner, 1982), transcription (Uddin et al., 1984; Wansink et al., 1993), and translation (Garlick et al., 1980). Performing such approaches have allowed for the characterization of cells actively undergoing macromolecular synthesis as well as quantification of synthesis rates, which facilitates the study of cell behavior during tissue remodeling, proliferation, stress, or disease (Yoshizawa et al., 1997; Rombouts et al., 2016; O'Brien and Lis, 1993; Rose et al., 1975).

In the past decade, bio-orthogonal macromolecule precursor analogs including 5-ethynyl-2′-deoxyuridine (EdU), 5-ethynyl-uridine (5-EU), and L-azidohomoalanine (AHA) have become commercially available for analysis of DNA synthesis, transcription, and translation, respectively. After injection of macromolecule precursor analogs into animals, precursors can later be detected in nascent DNA, RNA, and protein macromolecules with fluorescently labeled azides or alkynes through highly selective copper-catalyzed azide-alkyne cycloaddition (‘click’) chemistry (Kolb et al., 2001; Salic and Mitchison, 2008; Best, 2009). These powerful new analogs provide an alternative to the use of dangerous isotopes and the challenges associated with brominated precursors such as the requirement of large secondary antibodies (~150 kd) and harsh tissue retrieval methods that limit their use in whole tissue samples.

Advantages of the click-chemistry labeling approach include the inert nature of macromolecule precursor analogs that have minimal impact on the animal, the small size of fluorescently labeled alkynes and azides, and the high selectivity of click-chemistry. These advantages have enabled whole-mount fluorescent labeling of DNA synthesis (Salic and Mitchison, 2008), RNA transcription (Jao and Salic, 2008), protein translation (Hinz et al., 2012), and glycans (Sawa et al., 2006; Laughlin and Bertozzi, 2009) in animals. These pioneering proof-of-principle experiments have demonstrated that imaging macromolecular synthesis is possible, but the fact that most model organisms are large, optically opaque, and consist of heterogenous tissues has made it a challenge to image biological phenomena in deep tissues. Challenges such as photon penetration, differences in refractive indices among different cellular components, light-induced photodamage, and background fluorescence have limited the use of whole-mount imaging of macromolecular synthesis.

Advances in light sheet fluorescence microscopes (LSFMs) have recently enabled the imaging of large biological specimens from millimeters to several centimeters in size (Power and Huisken, 2017; Dodt et al., 2007; Keller and Dodt, 2012). LSFMs have been utilized for volumetric imaging of many varieties including visualization of mRNA in whole-mount fluorescence in situ hybridization experiments (Mano et al., 2018) and in vivo interrogation of deep tissue dynamics in transgenic reporter animals (Tomer et al., 2012), among others (Power and Huisken, 2017). Clearing methods like CLARITY (Chung et al., 2013), CUBIC (Susaki et al., 2014), and 3DISCO (Ertürk et al., 2012) have further enabled volumetric imaging by decreasing refractive index mismatches and tissue clearing to advance large specimen imaging even further. Together, the rise of three-dimensional imaging, new staining techniques, and tissue clearing has demanded new means for cell counting, segmentation, and fluorescence quantification.

Here we present a click-chemistry based method to visualize DNA synthesis, transcription, translation, and protein glycosylation in whole-mount samples using LSFM (Figure 1A). We demonstrate the utility of this technique by imaging macromolecular synthesis in the regenerating axolotl salamander limb. Following limb amputation, the axolotl regenerates its limb by generating a mass of proliferating cells at the limb stump called a blastema (Stocum, 2017). The blastema is an ideal environment to test our method because it is an accessible, heterogenous tissue that increases DNA synthesis, transcriptional output, and translation rates compared to uninjured tissue. Furthermore, DNA, RNA, and protein synthesis decrease after denervation of the regenerating limb (Singer and Caston, 1972; Dresden, 1969).

Outline of staining/analysis pipeline and exemplary images.

(A) Overview of entire sample preparation, imaging, and data analysis pipeline. (B–D) Once macromolecules are labeled in vivo, synthesis can be visualized throughout the injected animal. Here we show DNA synthesis (EdU) in the torso of a stage 52 larvae (B) and both DNA synthesis and protein glycosylation (GlcNAz) in the hand (C) and finger (D). For panels B-D, animals were pulsed with the corresponding macromolecule analog(s) for 3 hr. Images from panels B and C were uncleared and imaged at 5× magnification. Image from panel D was also uncleared and imaged at 10× magnification. Scale bars for panels B and C = 600 µm for each axis. Scale bars for panel D = 200 µm for each axis.

We use the regenerating limb to demonstrate that two click-it ready precursors can be administered and subsequently visualized simultaneously in a single sample. We also show that optical resolution of images can be improved with the clearing agent 2,2’-thiodiethanol (TDE), and that this method works in a number of tissues. We outline an image analysis pipeline for three dimensional (3D) morphology segmentation, cell counting, and fluorescence quantification of stained tissues. We apply this pipeline to the regenerating humerus to demonstrate the multiscale quantitative analysis capabilities of our method. Finally, we show that our method is sensitive enough to detect and quantify changes in DNA synthesis rates in whole-mount innervated and denervated regenerating limbs. Taken together, our method provides a unique approach to simultaneously interrogate cell state at the organ, cellular, and molecular levels of organization.

Results

Whole-mount, click-it based visualization of macromolecule synthesis

To visualize macromolecule synthesis, we injected click-it compatible monomer analogs (Table 1) intraperitonially 3 hr before sample collection. During this time, analogs metabolically incorporated into nascently synthesized macromolecules, resulting in in vivo labeling of macromolecule synthesis. Our group has found that collection of tissue after 3 hr provides sufficiently strong metabolic labeling of DNA synthesis (EdU), transcription (EU), translation (AHA), and protein glycosylation (GlcNAz). These labeled macromolecules contain either azide- or alkyne-modified monomers that can be detected with click-it compatible fluorescent molecules, enabling imaging of nascent macromolecules in whole-mount tissues with LSFM (Figure 1B–C). We show that our whole-mount method can be used at the organismal level to visualize the whole torso of a stage 52 axolotl larvae, where we observe proliferating cells in the developing limbs and hematopoiesis within the spleen (Figure 1B). To demonstrate the multiplexing capabilities of the approach, modified monomer analogs with disparate functional groups (EdU/AHA, 5-EU/AHA, EdU/GlcNAz, 5-EU/GlcNAz) were co-injected and visualized with LSFM (Figure 2A–D, Videos 13. For color blind accessible images, see Figure 2—figure supplement 1). whole-mount samples were comparable to 2D longitudinal tissue sections of the same stains (Figure 2I–L), showing that our method generates similar results in both whole-mount and tissue sections (Figure 2—figure supplement 2) with subcellular resolution (Figure 2—figure supplement 3). Furthermore, the number of EdU+ cells per blastema area in a single Z slice obtained by LSFM (EdU-AHA: 1954 cells/mm2, EdU-GlcNAz: 1458 cells/mm2) is similar to that of a tissue section obtained by confocal microscopy (EdU-AHA: 1272 cells/mm2, EdU-GlcNAz: 1349 cells/mm2). We finally demonstrate the specificity of GlcNAz incorporation by pretreating a GlcNAz-specific antibody on tissue sections collected from GlcNAz injected animals. By doing so, the subsequent click-it reaction was prevented due to the antibodies likely sterically hindering alkyne fluorophores from accessing the azide functional groups (Figure 2—figure supplement 4).

Figure 2 with 5 supplements see all
Dual staining of macromolecule synthesis in whole-mount imaging.

(A–D) Stitched and fused 3D reconstruction of 13 dpa blastemas stained for multiple macromolecules obtained by LSFM. (E–H) Single Z-plane from A-D that represents the entirety of the blastema. (I–L) Tissue section from identically treated limbs as A-H showing similar macromolecule staining patterns, indicating that the whole-mount staining method does not alter macromolecule synthesis staining patterns. Scale bars for panels A-D = 600 µm for each axis. Scale bars for panels E-L = 200 µm.

Table 1
Monomer analogs used to demonstrate the whole-mount visualization method.
NameMacromolecule analogBiological processClick-it modificationReference
5-ethynyl-2′-deoxyuridine (EdU)ThymidineDNA synthesisAlkyneSalic and Mitchison, 2008
5-Ethynyl Uridine (5-EU)UracilTranscriptionAlkyneJao and Salic, 2008
L-Azidohomoalanine (AHA)MethionineTranslationAzideWang et al., 2017
Azide-modified glucosamine (GlcNAz)GlucoseamineProtein glycosylationAzideLaughlin et al., 2006
Video 1
Rotating axolotl hand stained for EdU (green) and GlcNAz (red).
Video 2
Rotating axolotl limb stained for EdU (green), AHA (red), and DAPI (blue).
Video 3
Scroll through of Z-stack from Video 2.

An advantage of our method is that staining whole-mount tissues eliminates the need for sectioning, reducing the potential inconsistencies that arise as a result of the sectioning process (uneven tissue, different cutting planes, etc.). Additionally, traditional methods of obtaining 3D images of thick tissues with confocal microscopy are impractically slow when imaging hundreds of images in a single stack. LSFM allows for more rapid imaging of whole samples, requiring only minutes to image each sample. However, several considerations exist when imaging whole tissue in 3D with LSFM. Stain penetrates slower in whole 3D tissues compared to 10–20 µm thick tissue sections, requiring longer staining times. An advantage of our method is that the click reaction requires molecularly diminutive reagents that readily pass through cell and nuclear membranes, ensuring stain penetration in the center of dense tissues. Different refractive indices between disparate cellular components and the imaging media cause light to scatter, which can reduce the resolution and brightness of 3D images (Fadero and Maddox, 2017). To improve image resolution and light penetration, overnight refractive index matching with 67% 2,2’-thiodiethanol (TDE) can sufficiently clear axolotl limbs for imaging with LSFM. TDE was chosen due to its simplicity, cost, safety, and compatibility with common imaging modalities. This clearing method rapidly and effectively improves the signal-to-noise ratio of stained samples compared to imaging in PBS (Figure 2—figure supplement 5). Tissue morphology was minimally disturbed after clearing, with only mild shrinkage of the blastema epithelium observed. With careful attention to these challenges, our method provides a means to obtain high-quality 3D images from tissues 1 mm in depth in less than 10 min per sample with clear, consistent staining (Figure 2A–H, Figure 2—figure supplement 3).

3D, multiscale quantitative analysis of the regenerating humerus

To demonstrate that our whole-mount click-it method can obtain quantifiable data on the organ, cellular, and molecular levels of organization, we applied our technique to the regenerating humerus. After regenerating for 35 d, axolotls with mid-humeral amputations were injected with EdU/AHA to identify cells within the humerus undergoing DNA synthesis (EdU) and protein translation (AHA). We observed EdU staining in chondrocytes distal to the amputation plane and AHA staining in the humerus perichondrium (Figure 3A). We outline a multiscale, quantitative pipeline that leverages the staining patterns of these macromolecules for analysis of 3D humerus morphology and 3D macromolecule synthesis. This workflow combines available plugins in Fiji (Schindelin et al., 2012) and scripts developed in Fiji and Matlab (The MathWorks, 2019) for the data analysis process (see supplementary information for a detailed description).

Workflow for 3D, multiscale analysis of the regenerating axolotl humerus.

Multiscale analysis of a 35 dpa regenerating axolotl humerus, stained for AHA (red) and EdU (green). The humerus in the image stack was (A) aligned along the proximodistal (P–D) axis and (B) its morphology was segmented. The resulting mask was used to analyze the organ volume and shape by (D) reslicing it along the P-D axis and studying the cross-sections obtained. (C) The segmented morphology in B was used to mask the green channel for cellular- and molecular-level analyses. (E) Cells in the humerus were segmented and their spatial distribution was analyzed to obtain cellular number and density. (F) The masked image stack in C was resliced along the P-D axis and the pixel maps of the cross-sections were used to characterize the molecular intensity and distribution within the humerus. The vertical yellow line in A and C indicates the plane of amputation.

For 3D organ level analysis of macromolecule synthesis, AHA staining provided an adequate outline of the humerus, allowing us to segment its 3D morphology (Figure 3A–B). We quantified organ shape by assessing cross-sectional area (Figure 4A’) and circularity (Figure 4A”) of the segmented humerus along the proximodistal axis with BoneJ (Doube et al., 2010Figure 4A). These measurements of 3D organ size can be obtained with other methods such as microCT and focused ion beam scanning electron microscopy (FIB-SEM). However, microCT is unable to image every tissue due to stain limitations and uses hazardous radiation, while FIB-SEM can capture 3D surface topography but not the entire organ morphology. Our method has the capability to fully image the 3D structure of entire organs (size permitting) without the need for radiation while simultaneously capturing information from both the cellular and molecular levels of organization.

3D quantification across scales of a regenerating axolotl humerus.

(A) The cross-sections of the humerus in Figure 3D were analyzed with the Fiji plugin BoneJ to quantify humerus shape and volume. (A’) The cross-sectional area along the proximodistal (P–D) axis provides a measure of volume distribution along the humerus. (A”) The ratio of the maximum chord length from the minor axis (2 R1) with respect to the maximum chord length from the major axis (2 R2) provides a measure of cross-sectional circularity in the humerus. Values closer to 1.0 in the proximal side indicate a more circular cross-section in this zone. (B) The Fiji plugin Trainable Weka Segmentation 3D and 3D Objects Counter were used in the cellular analysis of proliferating chondrocytes illustrated in Figure 3E. (B’) The number of EdU+ cells within a 50 µm slice along the P-D axis was divided by the slice volume to obtain a density-like measure. (B”) The center of mass of each cell was plotted in 3D, with point size and color proportional to the segmented cell volume. (C) The molecular intensity and distribution were analyzed based on the resliced pixel intensity maps of the masked green channel in Figure 3F. (C’) Mean intensity of each slice perpendicular to the P-D axis. (C”) The histogram of the EdU staining in the humerus can provide a measure of the DNA synthesis rate. The vertical yellow line in the top row images indicates the plane of amputation.

On the cellular level, we segmented the 3D morphology of proliferating chondrocytes based on EdU staining using the Trainable Weka Segmentation 3D plugin (Arganda-Carreras et al., 2017Figure 4B”). With this segmentation, we identify highly condensed regions of cells undergoing rapid rates of DNA synthesis (Figure 4B). From these data, we observe cells synthesizing DNA most abundantly distal to the plane of amputation, as expected for dividing chondrocytes. Traditionally, cell quantification as such is conducted on 2D tissue sections. In heterogenous tissues, however, cells are distributed non-uniformly; 2D sampling may not accurately capture the cellular distribution and cannot be used to determine cell volume or shape. Our whole-mount staining method allows quantification of cells within an entire 3D tissue, resulting in a more accurate assessment of cell density within heterogenous tissues.

To demonstrate the quantitative molecular analysis of 3D macromolecule synthesis, we assessed the staining intensity of EdU in slices along the proximodistal axis of the regenerating humerus (Figure 4C). EdU intensity represents the rate at which a cell undergoes DNA synthesis, which is one of the first steps of cell division and proliferation. Proliferating cells rapidly synthesize DNA, which enables these cells to integrate EdU into nascent DNA strands. This provides ample opportunities for covalent linkage of fluorescent molecules to the DNA strand. Thus, higher pixel values within EdU+ cells are representative of more DNA synthesis in the cell. These data show that EdU intensity is strongest in the regions distal to the amputation plane of the humerus (Figure 4C’), providing a quantitative measure to assess macromolecule synthesis on the molecular level. Within a tissue, cells synthesize macromolecules heterogeneously, reflected by different fluorescence values between cells. Thus, quantifying macromolecule synthesis based on the presence/absence of signal instead of fluorescence does not account for variability in macromolecule synthesis rates among cells. To compound this issue, quantifying fluorescence in tissue sections only represents the rate of macromolecule synthesis from a fraction of cells in larger, heterogenous tissues. Our method provides a means to capture this molecular heterogeneity in 3D samples, allowing us to observe whole 3D regions in the regenerating humerus that synthesize DNA more rapidly than others, which further demonstrates the utility of our whole-mount click staining method.

Taken together, these results demonstrate that our method can provide quantifiable data on the organ, cellular, and molecular levels of organization. This highlights the novelty of our method, as we have not found previous examples of multiscale analysis as outlined here. We foresee this multiscale, quantitative analysis having broad applications in the examination of dynamic cell processes in 3D, such as in cancer metabolism and mammalian neurogenesis or other fields where macromolecule synthesis is traditionally studied in tissue sections.

3D, molecular analysis of biological perturbations

To demonstrate that our method is sensitive enough to detect subtle changes in macromolecule synthesis in vivo, we quantified the difference in EdU intensity between limbs regenerating with and without a nerve supply. Blastema cells are thought to have a cell cycle length of 40–50 hr, with S phase approximately 30 hr (Tassava et al., 1987). In newts, it has been shown that amino acid, RNA, and DNA analog incorporation decreases approximately 30% by 24 hr after denervation (Singer and Caston, 1972). Based upon these estimates, we amputated both forelimbs at the mid-humerus and denervated the left limb at the brachial plexus 24 hr before collection. This timepoint for denervation was chosen because it should be sufficiently long to have an impact on DNA, RNA, and protein synthesis rates. At 6, 9, 12, 15, 18, and 25 d post-amputation (dpa) animals were pulsed with EdU for 3 hr before collection to label proliferating blastema cells (Figure 5A). We chose 3 hr of EdU incorporation to provide a snapshot of DNA synthesis in the 30 hr total S phase of blastema cells. LSFM was used to image samples (Figure 5B), ensuring pixel resolution was consistent between samples. We quantified DNA synthesis in denervated limbs compared to innervated limbs by creating a 175 × 175 × 175 µm cube 250 µm from the distal most tip of the blastema (Figure 6A). The size and location of the quantification cube was chosen to maximize the number of EdU+ mesenchymal cells and to exclude any epithelial cells. Although we limited the cube to the size of the smallest blastema, these parameters can be customized depending on the size of the blastemas. For this study, we estimate 50–150 blastema cells are found within the cube. From these results, we observed a marked decrease in blastema EdU incorporation due to denervation at 9, 12, and 15dpa (Figure 6B, Figure 6—figure supplement 1), demonstrating that our whole-mount staining approach is capable of detecting changes in macromolecule synthesis after biological perturbations. One potential limitation of our method in the blastema is the inability to perform single cell segmentation. This is due to the density and abundance of cells within the blastema. We predict that higher magnification imaging and deep learning segmentation techniques may overcome this limitation but will significantly increase imaging time and file size. For comparison, we have included estimates of imaging time and file sizes for confocal microscopy with a 20X objective and LSFM with a 5× objective (Supplementary file 1).

3D visualization of DNA synthesis in innervated/denervated regenerating limbs.

(A) Schematic of experimental design used to obtain samples from B-O. (B–O) Time course of regeneration in innervated and 24 hr denervated limbs at 0, 6, 9, 12, 15, 18, and 25 dpa. Scale bars for panels B-O = 600 µm for each axis.

Figure 6 with 1 supplement see all
3D quantification of DNA synthesis in innervated/denervated regenerating limbs.

(A) A cube with sides of 175 µm was cropped along the proximodistal axis 250 µm from the distal tip of each blastema. P = proximal, D = distal. (B) Violin plots illustrate the pixel intensity of the innervated vs denervated blastema cubes. Comparison of mean intensity values (marked with a cross) of the same animal confirms that innervated blastemas have faster DNA synthesis rates than their denervated counterparts.

Discussion

The work presented here provides a fast, simple pipeline for visualizing macromolecule turnover in the 3D space of whole tissues. While tissue sections were previously the standard for studying these cellular processes, a new standard in the field must be expected where dynamic processes like macromolecule synthesis are visualized in 3D to obtain a more complete understanding of how these processes occur in a larger tissue context. Few modalities of imaging exist to provide this level of analysis. Here, we outline a method to study macromolecule synthesis at the organ, cellular, and molecular levels of organization, which is important in understanding cell state and how cell state affects neighboring cells and tissues. To this end, we show high levels of DNA synthesis, transcription, translation, and protein glycosylation in the entire 3D space of the regenerating blastema after limb amputation and that these processes can be visualized concurrently. Our lab has also demonstrated this whole-mount technique in other axolotl tissues including the lung (Jensen, 2018). We outline a multiscale pipeline for analysis and quantification of heterogeneous tissues at the organ, cellular, and molecular levels of organization. Further, we demonstrate that our method is sensitive to detect biological perturbations by showing a decrease in DNA synthesis in the blastema following limb denervation.

We foresee our method being used to similarly readdress other classical questions with modern techniques for a more exhaustive understanding of biological processes; traditional questions within the fields of cancer biology and neurobiology may especially benefit from technology as such. Additionally, as more click-it ready macromolecules monomer analogs are generated, our method will provide a means to study more biological processes in whole tissues. Finally, we expect that this method should be amenable both with other staining techniques such as whole-mount immunohistochemistry and to a number of animal models, including mouse and zebrafish.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Ambystoma mexicanum)d/d axolotlAmbystoma genetic stock centerRRID:AGSC_101L
Commercial assay, kit5-Ethynyl-2’-deoxyuridine
(EdU)
clickchemistrytools.comCat# 1149Monomer analog
Commercial assay, kit5-Ethynyl Uridine (5-EU)clickchemistrytools.comCat# 1261Monomer analog
Commercial assay, kitL-Azidohomoalanine (AHA)clickchemistrytools.comCat# 1066Monomer analog
Commercial assay, kitN-azidoacetylglucosamine-tetraacylated (Ac4GlcNAz)clickchemistrytools.comCat# 1085Monomer analog
Commercial assay, kitAFDye 488 Azideclickchemistrytools.comCat# 1275Fluorescent azide
Commercial assay, kitAFDye 594 Azideclickchemistrytools.comCat# 1295Fluorescent azide
Commercial assay, kitAFDye 594 Alkyneclickchemistrytools.comCat# 1297Fluorescent alkyne
Chemical compound, drug2,2′-Thiodiethanol (TDE)SigmaCat# 166782Clearing agent
Chemical compound, drugL(+)-Ascorbic acid sodium saltThermoCat# AC352680050Click-it cocktail component
Chemical compound, drugCopper(II) sulfate pentahydrate, 98+%ThermoCat# AC423615000Click-it cocktail component
Chemical compound, drugSlowFade Gold Antifade MountantThermoCat# S36936Mountant

Animal procedures

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Axolotls were either bred in captivity at Northeastern University or purchased from the Ambystoma Genetic Stock Center at the University of Kentucky. Experiments were performed in accordance with the Northeastern University Institutional Animal Care and Use Committee. Animals were grown to 4–6 cm (Mean 5.3 cm, SD 0.36) and 1–1.5 g (Mean 1.3 g, SD 0.19 g) for use in all studies. For all experiments, animals were anesthetized by treatment of 0.01% benzocaine until visually immobilized. Limbs were amputated either at the distal end of the zeugopod or midway through the stylopod, and bones were trimmed below the amputation plane to allow for uniform growth. At the date of collection, animals were reanesthetized and injected with either 5-ethynyl-2′-deoxyuridine (EdU) to identify proliferating cells (8.0 µg/g animal), 5-Ethynyl Uridine (5-EU) to label RNA (270.0 µg/g animal), L-Azidohomoalanine (AHA) to label protein (180.59 µg/g animal), or N-azidoacetylglucosamine-tetraacylated (GlcNAz) to label glycosylated proteins (430 µg/g animal) alone or simultaneously in the following combinations: EdU/AHA, EdU/GlcNAz, 5-EU/AHA, 5-EU/GlcNAz (Table 1). All monomer analogs were purchased from www.clickchemistrytools.com and resuspended in DMSO at the following concentrations: EdU- 300 mM, 5-EU- 100 mM, AHA- 100 mM, GlcNAz- 100 mM. Stocks were further diluted in 1× phosphate buffered saline (PBS) for injection. After 3hr of analog incorporation, limbs were collected from the upper stylopod and fixed in 4% paraformaldehyde (PFA) (diluted in 1× PBS) at 4°C overnight. If limbs were denervated, the nerve supply was severed at the brachial plexus 24 hr before tissue collection.

Whole-mount click-it protocol

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Following fixation in 4% PFA, samples were washed three times with 1X PBS at room temperature (~23°C) for 5 min. Samples were dehydrated in an increasing methanol series at room temperature starting with 25% methanol (diluted in 1× PBS), 50% methanol, 75% methanol, and 100% methanol for 5 min at each step. Samples could then be stored in 100% methanol indefinitely at −20°C. For staining, samples were rehydrated in a decreasing methanol series starting with 75% methanol (diluted in 1× PBS), 50% methanol, 25% methanol, and finally placed in 100% 1× PBS for 5 min at each step. Samples were then washed three times with 1× PBST (1× PBS with 0.1% Triton) for 5 min at room temperature. To aid in clearing, samples were washed in 0.5% trypsin (diluted in 1× PBS) for 30–90 min on a rocker at room temperature, or until the sample appeared translucent. Samples were washed three times at room temperature for 5 min with deionized water, then washed in 100% acetone for 20 min at −20°C and washed with deionized water again for 10 min. Samples were washed in 1× PBST three times at room temperature for 5 min before applying click-it cocktail for overnight at room temperature. The click-it cocktail was made in 500 µl of 1× TRIS buffered saline as follows: 50 µL 1M sodium ascorbate (100 mM final), 20 µL 100 mM CuSO4 (4 mM final), and 2 µL 500 µM azide- or alkyne-modified Alexa Flour (2 µM final), combined in order as listed. After the first round of staining, samples were washed at room temperature six times for 30 min with rocking. For double-labelling, samples were again placed in the click-it cocktail with a different fluorescent dye to stain for the second analog at room temperature overnight. Both rounds of staining were conducted in the dark to prevent photodegradation of fluorescent molecules. For staining with DAPI, samples were washed three times for 5 min with 1× PBS, then placed in 2.86 µM DAPI for 4 d at room temperature. Samples were washed three times for 20 min with 1× PBS and left in 1× PBS at 4°C for short-term storage before imaging with LSFM. whole-mount samples were cleared with 67% TDE (diluted in 1× PBS) overnight at room temperature in the dark.

Tissue section click-it protocol

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Following fixation in 4% PFA, samples were washed three times in 1× PBS each for 5 min, and cryoprotected in 30% sucrose on a rocker until the tissue fully sank. Samples were removed form sucrose and briefly washed in optimal cutting temperature (OCT) compound before mounting in OCT compound and frozen at −80°C. A cryostat was used to obtain 10 µm sections, and slides were baked at 65°C for 15 min. Slides were then washed with water for 30 min at room temperature to remove residual OCT. Slides were washed once with 1× PBS for 5 min at room temperature. The click-it cocktail (same as above) was applied to the slides and incubated at room temperature in the dark for 30 min. If staining for a second macromolecule, slides were washed five times for 5 min with 1× PBS at room temperature. The samples were then stained for 30 min at room temperature in the dark using the above click-it cocktail with a different a fluorophore dye. Following the final click-it reaction, slides were washed once with 1× PBS at room temperature for 5 min, then stained with 2.86 µM DAPI for 5 min at room temperature. Slides were washed again with 1× PBS for 5 min at room temperature and water for 5 min at room temperature and mounted with SlowFade Gold Antifade Mountant. Slides were imaged using a Zeiss LSM800 confocal microscope.

Light sheet microscopy

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All 3D images were acquired using a Zeiss light sheet Z.1 microscope paired with Zen software. Unless otherwise indicated, samples were cleared and imaged in 67% TDE. Post-processing for visualization purposes was performed with Arivis Vision4D v3.1.4 on a workstation with a 64-bit Windows Embedded Standard operating system, and an Intel(R) Xeon(R) CPU E5-2620 v3 @ 2.40 GhZ (two processors), 128 GB RAM, and NVIDIA Quadro K2200 GPU. Sub-volumes were stitched together with the Tile Sorter in Arivis, using the manual projection option. Volume fusion was performed through automatic landmark registration of manually selected points for alignment. For visualization, background intensity was corrected using the automatic functionality.

Data analysis

All data were processed on desktop computers with the aid of Fiji (Schindelin et al., 2012) and Matlab (The MathWorks, 2019). The custom Fiji scripts and Matlab codes used are available in the supplementary material. We performed all analyses on unprocessed. czi files acquired directly from the light sheet microscope.

Organ level

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The goal of the organ level analysis was to determine gross morphology of the organ, such as area, principal axes, and maximum diameters in said axes for each cross section. Critical to this step is alignment of the images to a standard defined axis and reslicing in the transverse direction (perpendicular to the long axis of the humerus). The image stack of a regenerating axolotl elbow was imported into Fiji and aligned along the proximodistal axis of the humerus (Figure 3A). Morphological segmentation of the humerus was performed semi-automatically with the Segmentation Editor plugin (Figure 3B). The segmented surface was then exported from Fiji as a mesh via an. stl file using 3D Viewer (Pietzsch et al., 2015). Volumetric analysis was performed in Fiji by reslicing the aligned mask (Figure 3B) to obtain a stack of cross-sections perpendicular to the proximodistal axis, and then quantified the shape and volume with the Slice Geometry option in the BoneJ (Doube et al., 2010) Fiji plugin. Slice Geometry calculates cross-sectional geometric properties of shapes, including area, second moment of area around the major and minor axes, and maximum chord length from these major and minor axes.

Cellular level

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The goal of the cellular level analysis was to determine the number of proliferating cells (EdU+) as a function of proximodistal position along the humerus, as well as average size, shape, and orientation. The aligned image stack (Figure 3A) was cropped using the morphological segmentation (Figure 3B) as a mask with the aid of the Image Calculator in Fiji. Based on the EdU staining (Figure 3C), we segmented the nuclei of the proliferating cells with Trainable Weka Segmentation 3D (Arganda-Carreras et al., 2017). We used a combination of filters available in Fiji before and after training the algorithm to improve segmentation results. Filters applied include the 3D Edge and Symmetry, Background Subtraction, 3D Fill Holes, Gaussian Blur 3D, and 3D Watershed Split. A detailed description of the sequence and parameters used is available in the supplementary materials. The 3D Objects Counter then provided a list of identified cells as well as their volume and position, among other information. In addition, the surfaces of the segmented cell nuclei were exported and processed in MeshLab, similarly to the process followed with the organ segmentation, to be visualized in Matlab (Figure 3E). The centroid coordinates and corresponding object volumes identified in Fiji were imported into Matlab for further processing and plotting. We computed the number of cell centroids within 50 μm thick slices perpendicular to the proximodistal axis to obtain a density-like measure of proliferating cells. Slice thickness was selected as slightly larger than the average cell diameter to ensure cells were not sampled across more than two slices.

Molecular level

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The goal of the molecular level analysis was to determine molecular activity, characterized by fluorescent signal, which in our study represented DNA replication (EdU). The aligned and cropped image stack used as a starting point of the cellular-level analysis (Figure 3C) was also analyzed at the molecular level. We resliced the image stack along the proximodistal axis in the organ under study (Figure 3F). The histogram and pixel intensity statistics were listed for each slice via the getRawstatistics function. These allow for further quantification of the fluorescent signal in Matlab, for example, we calculated the mean intensity of each plane along the proximodistal axis and the histogram of the whole organ. For the example showing nerve-dependent regeneration in axolotl blastemas (Figure 6A), we compared overall pixel intensity of the left (denervated) and right (control, innervated) forelimbs of the same animal. To ensure full repeatability of our data analysis, we selected the same cubic volume in all blastemas processed: a cube with 175 μm sides, centered along the proximal-distal axis and at a distance of 250 μm from the distal tip (Figure 6A). The cube size was adjusted to maximize the cube volume for all blastemas processed, while ensuring that the entire cube was contained within the blastema. Histograms of each cubic volume and the ratio between the innervated and denervated limb of the same animal were computed and plotted with Matlab (Figure 6B) to quantify the changes in proliferation in innervated versus denervated limbs at different stages of regeneration. All scripts for blastema cube quantification are provided in the supplementary materials.

Data availability

Original data and scripts used to process the data are available at Northeastern University’s Digital Repository http://hdl.handle.net/2047/D20388308.

The following data sets were generated

References

    1. Rose RJ
    2. Cran DG
    3. Possingham JV
    (1975)
    Changes in DNA synthesis during cell growth and chloroplast replication in greening spinach leaf disks
    Journal of Cell Science 17:27–41.
  1. Software
    1. The MathWorks
    (2019)
    I. Symbolic Math Toolbox
    I. Symbolic Math Toolbox.

Decision letter

  1. Didier YR Stainier
    Senior Editor; Max Planck Institute for Heart and Lung Research, Germany
  2. Tatiana Sandoval-Guzman
    Reviewing Editor; Center for Regenerative Therapies TU Dresden, Germany
  3. Tatiana Sandoval-Guzman
    Reviewer; Center for Regenerative Therapies TU Dresden, Germany

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This study presents a combination of techniques to examine macromolecule synthesis in a complex and rapidly changing structure such as the regenerating limb of an axolotl. A pipeline for 3D visualization and quantification reveals the heterogenous cellular response within a complex tissue. Of interest, is the compatibility to use in other organs and tissues or at the organismal level.

Decision letter after peer review:

Thank you for submitting your article "3D Visualization of Macromolecule Synthesis" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Tatiana Sandoval-Guzman as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Didier Stainier as the Senior Editor.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission. This decision includes a summary redacted from the three reviews.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.

Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in eLife, either of which would be linked to the original paper.

Summary:

Duerr and colleagues present a combination of click-it, tissue clearing and light sheet technologies to examine macromolecule synthesis (namely DNA synthesis, transcription, translation, and glycosylation). This pipeline allows the visualization and quantification of cellular processes in growing 3-dimensional tissue. The authors use axolotl limb regeneration, a challenging system to tackle given its complex tissue types and large volume. Upon amputation, the remaining tissue in the limb will produce proliferating progenitors that accumulate forming a structure called the blastema, which the authors use to test macromolecule synthesis simultaneously. The authors demonstrate that administration of macromolecule analogs can be visualized simultaneously in a single sample. Furthermore, the authors create an image analysis pipeline that allows the analysis of individual elements or segments within the limb; they use the regenerating humerus to demonstrate the multiscale quantitative analysis capabilities of their method.

While the technologies used in this manuscript are not novel, the combination of these techniques together provide a compelling foundation to answer questions in this and other similar systems, while decreasing labor-intensive techniques such as tissue sectioning. The ability to look into these processes while preserving structure, helps understand the underlying biological function of a cell and its intricate relation to other tissues. This method opens up the possibility to analyze complex heterogeneous changes across heterogeneous tissues.

Overall, this manuscript has a potential broad scope and by using already commercial and open source tools, it can facilitate a broader applicability.

We have the following suggestions to improve the validation of the methods described in this manuscript.

Essential revisions:

1) One of the strengths of this methods is the diminution of a labor-intensive sectioning/staining/imaging track. One concern is that a more persuasive effort could be presented to validate the number of cells identified by both methods. Using the EdU+ quantification as a point of reference between the imaging protocols, the authors could provide a direct comparison with the standard methodology. One possibility is a randomized quantification of single planes from the wholemount volume and tissue sections. This would demonstrate side by side the detection sensitivity of the whole mount/LSFM visualization. Additionally, there is no information related to imaging parameters such as magnification, pixel size and its comparison between wholemount light sheet and confocal imaging. Many of the figures would be greatly enhanced by zoom insets to show the level of resolution and a comparison of resolution between the two methods. Can the same conclusion be reached using two-dimensional section imaging as it can using 3-dimensional imaging?

2) Given the complex nature of a regenerating limb, we consider of importance the biology behind the methods, more information is needed describing the rationale for the given experimental choices. In general, there is very little information shown for the methods. For example, macromolecule analogs were injected three hours before tissue harvesting. Why was this timepoint chosen? Is this time choice relevant for the additional analogs AHA or GlyNAz? Is there an optimization that the authors didn't mentioned? Similarly, the authors do not explain the use of TDE as a clearing agent versus other clearing strategies that they highlight in the manuscript. Is there an advantage or reason for this choice? The authors should clearly state if there is.

3) A key point of the paper is the simultaneous visualization and quantification of macromolecules. While the authors demonstrate that co-administration of macromolecule analogs is possible for simultaneous visualization, the further analysis doesn't exploit this important feature. We wonder if this method allows for identification of cells through immunofluorescence/immunohistochemistry together with the click-it macromolecule labeling. Did the authors attempt to immunolocalize a particular cell population? One particular question that we would like to see addressed is the quantification of RNA or protein synthesis in double labelled tissue (in volume). The authors may already have tissue that is double labeled.

4) How applicable can this method be for other tissues/organisms, or is it limited to more dense tissue (bone)? Have the authors attempted this technique on other organs? If it is envisioned to replace tissue sectioning, the authors should address whether the technique is adaptable to other tissues and model organisms. If generalizable, the associated supplementary tools and tutorial should also reflect this (e.g., the ImageJ macro currently prompts for a "blastema file").

5) The methods and rationale for the limb denervation experiments should be better explained. Specifically, why was a mock denervation not needed? In the absence of this control, could the change in DNA synthesis be due to the injury of the denervation, rather than an absence of nerves? Please also include more details on the time point chosen to denervate the limb, how the time point analyzed (24 hrs post denervation) relates to the timing of nerve degeneration and why such a small volume was analyzed. One possibility is that a small volume could bias the final histogram measurement. Did the authors consider analyzing two volumes in the blastema, for example, one proximal and one distal?

6) Figure 6 can show the power of the method, however, there is no statistical test to quantify the difference between the two experimental groups. This computational pipeline is giving numerical values to a biological process, it is worth then to analyse them. The graph in Figure 6 shows only the histograms of the 3 time points that the authors considered affected. It would be informative to know how the time points that show no change look in comparison. In general, it is challenging to interpret Figure 6, we suggest separating the histograms, or using violin plots for better visualization.

https://doi.org/10.7554/eLife.60354.sa1

Author response

Essential revisions:

1) One of the strengths of this methods is the diminution of a labor-intensive sectioning/staining/imaging track. One concern is that a more persuasive effort could be presented to validate the number of cells identified by both methods. Using the EdU+ quantification as a point of reference between the imaging protocols, the authors could provide a direct comparison with the standard methodology. One possibility is a randomized quantification of single planes from the wholemount volume and tissue sections. This would demonstrate side by side the detection sensitivity of the whole mount/LSFM visualization. Additionally, there is no information related to imaging parameters such as magnification, pixel size and its comparison between wholemount light sheet and confocal imaging. Many of the figures would be greatly enhanced by zoom insets to show the level of resolution and a comparison of resolution between the two methods. Can the same conclusion be reached using two-dimensional section imaging as it can using 3-dimensional imaging?

We have included estimates of cell counts in LSFM vs. confocal images. The line reads “Furthermore, the number of EdU+ cells per blastema area in a single Z slice obtained by LSFM (EdU-AHA: 1954 cells/mm2, EdU-GlcNAz: 1458 cells/mm2) is similar to that of a tissue section obtained by confocal microscopy (EdU-AHA: 1272 cells/mm2, EdU-GlcNAz: 1349 cells/mm2).”

We have included Supplementary file 1 with imaging parameters for LSFM and confocal imaging.

We have included Figure 2—figure supplement 3 to show the resolution that can be obtained with empty magnification of a picture obtained with LSFM.

2) Given the complex nature of a regenerating limb, we consider of importance the biology behind the methods, more information is needed describing the rationale for the given experimental choices. In general, there is very little information shown for the methods. For example, macromolecule analogs were injected three hours before tissue harvesting. Why was this timepoint chosen? Is this time choice relevant for the additional analogs AHA or GlyNAz? Is there an optimization that the authors didn't mentioned? Similarly, the authors do not explain the use of TDE as a clearing agent versus other clearing strategies that they highlight in the manuscript. Is there an advantage or reason for this choice? The authors should clearly state if there is.

Our goal for the pulse labelling was to obtain the strongest signal, while providing information on only the newest macromolecules. We have found that strong labelling occurs at 3 hours of labeling for EdU. We have observed from previous experience that 1 hour EU labeling provides a weak signal while 3 hours provides a signal comparable to a 3 hour EdU pulse. For consistency with these results, we presumed that 3 hours of AHA and GlcNaz labeling would also provide strong labeling of translation and glycosylation in action. We clarified this information in the text. The lines read “Our group has found that collection of tissue after three hours provides sufficiently strong metabolic labeling of DNA synthesis (EdU), transcription (EU), translation (AHA), and protein glycosylation (GlcNAz).”

We chose TDE based upon its simplicity, cost, safety, and compatibility with common imaging objectives. We include our rationale for using TDE as our clearing reagent. The lines read “TDE was chosen due to its simplicity, cost, safety, and compatibility with common imaging modalities.”

3) A key point of the paper is the simultaneous visualization and quantification of macromolecules. While the authors demonstrate that co-administration of macromolecule analogs is possible for simultaneous visualization, the further analysis doesn't exploit this important feature. We wonder if this method allows for identification of cells through immunofluorescence/immunohistochemistry together with the click-it macromolecule labeling. Did the authors attempt to immunolocalize a particular cell population? One particular question that we would like to see addressed is the quantification of RNA or protein synthesis in double labelled tissue (in volume). The authors may already have tissue that is double labeled.

We suspect that our method would be compatible with immunohistochemistry. However, whole mount immunohistochemisty is still a developing technology in the axolotl. Our labs are working to optimize this technology, but for the purpose of this paper we wished to demonstrate a method for which macromolecule synthesis can be visualized and quantified in 3D.

Quantification of macromolecule synthesis in double labelled tissue is an important question. We suspect that the method we developed for quantifying pixel intensity within a 3D cube would be sufficient to also quantify nascent RNA and protein synthesis in the blastema. Unfortunately, we do not have tissue ready for imaging, but plan on running these experiments in the future. Given the circumstances surrounding the COVID-19 pandemic with limited people in our laboratory and limited microscope availability, it would be difficult for this experiment to be completed by the time revisions are due.

We added the following lines in the Discussion to address compatibility with these methods, “Finally, we expect that this method should be amenable both with other staining techniques such as whole mount immunohistochemistry and to a number of animal models, including mouse and zebrafish.”

4) How applicable can this method be for other tissues/organisms, or is it limited to more dense tissue (bone)? Have the authors attempted this technique on other organs? If it is envisioned to replace tissue sectioning, the authors should address whether the technique is adaptable to other tissues and model organisms. If generalizable, the associated supplementary tools and tutorial should also reflect this (e.g., the ImageJ macro currently prompts for a "blastema file").

Yes, this method is amenable to a number of tissues. We show an example of staining entire larvae in Figure 1, which we now highlight explicitly in the manuscript. The lines now read “We show that our whole mount method can be used at the organismal level to visualize the whole torso of a stage 52 axolotl larvae, where we observe proliferating cells in the developing limbs and hematopoiesis within the spleen (Figure 1B).”

We also have a manuscript uploaded to BioRxiv that shows whole mount EdU staining of the axolotl lung. We now reference this manuscript in the Discussion. The lines now read “Our lab has also demonstrated this whole mount technique in other axolotl tissues including the lung (Jensen et al., 2018).”

We have modified the ImageJ macro for generalizable use.

5) The methods and rationale for the limb denervation experiments should be better explained. Specifically, why was a mock denervation not needed? In the absence of this control, could the change in DNA synthesis be due to the injury of the denervation, rather than an absence of nerves? Please also include more details on the time point chosen to denervate the limb, how the time point analyzed (24 hrs post denervation) relates to the timing of nerve degeneration and why such a small volume was analyzed. One possibility is that a small volume could bias the final histogram measurement. Did the authors consider analyzing two volumes in the blastema, for example, one proximal and one distal?

The surgery for a mock denervation involves opening a small area of epithelium in the axilla of the limb. Once this area is opened, the nerves can be accessed and cut. Johnson et al. 2018 (DOI: 10.1016/j.ydbio.2017.07.010) showed that skin injuries on the limb have little impact on EdU incorporation. For this reason, we deemed it unnecessary to use a mock denervation. This has also not generally been the standard in limb denervation studies in the past, although we see the utility for rigorous experimental design in the future. Our assumptions are based upon a series of experiments from Marcus Singer between 1942-1950 showing it is the absence of a sufficient motor, sensory, and sympathetic innervation that supports proliferation during regeneration and not an injury alone. As an example, in 1943 Singer cut the dorsal and ventral roots of dorsal root ganglia 3, 4, and 5 and showed that limbs still regenerated comparable to contralateral control amputated limbs. This demonstrates that as long as sufficient nerve axons reach the limb, it will regenerate. It is the lack of these axons in the limb after denervation that leads to a lack of proliferation. As examples of a lack of sham control through the years using salamander denervation, see Butler and Schotte, 1941 (Histological alterations in denervated non-regenerating limbs of urodele larvae); Olsen et al., 1984 (Rescue of blocked cells by reinnervation in denervated forelimb stumps of larval Ambystoma); Wu et al., 2013 (de novo transcriptome sequencing of axolotl blastema for identification of differentially expressed genes during limb regeneration). We agree with the reviewer though that this should be a standard control in the field moving forward.

More information on the 24-hour denervation timepoint was included in the text. The lines read “Blastema cells are thought to have a cell cycle length of 40-50 hours with S phase approximately 30 hours (Tassava, Goldhamer and Tomlinson, 1987). […] Three hours of EdU incorporation was chosen to provide a snapshot of DNA synthesis in the 30 hour total S phase of blastema cells.”

More information on the size/location of the quantification cube was included in the text. We estimate that 50-150 blastema cells are located within the quantification cube. In terms of the cube that was measured in limbs, we generated the largest cube possible to fit within all blastemas at all time points. Larger fields of view could be chosen in later blastema stages, but for comparison across time points, we decided to limit our analysis to the largest cube available across all limbs. Also, we chose to exclude the epithelium as this tissue has different cell cycle dynamics compared to the mesenchyme during regeneration. The new lines now read “The size and location of the quantification cube was chosen to maximize the number of EdU+ mesenchymal cells and to exclude any epithelial cells. […] For this study, we estimate 50-150 blastema cells are found within the cube.”

6) Figure 6 can show the power of the method, however, there is no statistical test to quantify the difference between the two experimental groups. This computational pipeline is giving numerical values to a biological process, it is worth then to analyse them. The graph in Figure 6 shows only the histograms of the 3 time points that the authors considered affected. It would be informative to know how the time points that show no change look in comparison. In general, it is challenging to interpret Figure 6, we suggest separating the histograms, or using violin plots for better visualization.

With a sample size of two in each group, we feel that a statistical test in this case would be of little consequence and not informative. With our multiscale analysis pipeline, we simply wished to demonstrate that macromolecule synthesis rates can be quantified using pixel intensity. This is also why we chose to analyze three time points. While we agree the biology would be interesting, we feel that the scope of our paper is to demonstrate the capabilities of our method to address biological questions. It is for this reason that we chose a well characterized biological perturbation such as denervation to demonstrate our method.

We now represent the data with violin plots in Figure 6 and it represents the data in a clearer manner. It is also obvious now after two biological replicates that the difference between innervated and denervated limbs is in accordance with the known impacts of limb denervation. We have also separated the histograms and included them in Figure 6—figure supplement 1.

https://doi.org/10.7554/eLife.60354.sa2

Article and author information

Author details

  1. Timothy J Duerr

    Department of Biology, Northeastern University, Boston, United States
    Contribution
    Formal analysis, Investigation, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4945-0294
  2. Ester Comellas

    1. Department of Mathematics, Laboratori de Càlcul Numeric (LaCàN), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
    2. Department of Mechanical and Industrial Engineering, Northeastern University, Boston, United States
    Contribution
    Data curation, Software, Formal analysis, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3981-2634
  3. Eun Kyung Jeon

    Department of Biology, Northeastern University, Boston, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  4. Johanna E Farkas

    Department of Biology, Northeastern University, Boston, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6540-7870
  5. Marylou Joetzjer

    University of Technology of Compiègne, Compiègne, France
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  6. Julien Garnier

    University of Technology of Compiègne, Compiègne, France
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  7. Sandra J Shefelbine

    1. Department of Mechanical and Industrial Engineering, Northeastern University, Boston, United States
    2. Department of Bioengineering, Northeastern University, Boston, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4011-9044
  8. James R Monaghan

    1. Department of Biology, Northeastern University, Boston, United States
    2. Institute for Chemical Imaging of Living Systems, Northeastern University, Boston, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing
    For correspondence
    j.monaghan@northeastern.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6689-6108

Funding

National Science Foundation (1727518)

  • Sandra J Shefelbine

Northeastern University (Matz Scholarship)

  • Eun Kyung Jeon

Northeastern University (Undergraduate Research Fellowship)

  • Eun Kyung Jeon

National Science Foundation (1656429)

  • James R Monaghan

National Science Foundation (1558017)

  • James R Monaghan

European Commission (MSCA-GF 841047 CompLimb)

  • Ester Comellas

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors would like to acknowledge Tyler Jensen for his early assistance in developing the whole-mount staining protocol, and both Alex Lovely and Gouxin Rong for their microscopy and image analysis assistance. Images were obtained from the Harvard University Center for Biological Imaging and the Northeastern University Chemical Imaging of Living Systems core. We thank the Institute for Chemical Imaging of Living Systems at Northeastern University for consultation and imaging support. We acknowledge animal support from the Ambystoma Genetic Stock Center funded by NIH grant P40-OD019794.

Ethics

Animal experimentation: Axolotls (Ambystoma mexicanum: d/d RRID Catalog #101L) were either bred in captivity at Northeastern University or purchased from the Ambystoma Genetic Stock Center at the University of Kentucky. Experiments were performed in accordance with Northeastern University Institutional Animal Care and Use Committee. Animals were grown to 4-6cm (Mean 5.3cm, SD 0.36) and 1-1.5g (Mean 1.3g, SD 0.19g) for use in all studies. For all experiments, animals were anesthetized by treatment of 0.01% benzocaine until visually immobilized.

Senior Editor

  1. Didier YR Stainier, Max Planck Institute for Heart and Lung Research, Germany

Reviewing Editor

  1. Tatiana Sandoval-Guzman, Center for Regenerative Therapies TU Dresden, Germany

Reviewer

  1. Tatiana Sandoval-Guzman, Center for Regenerative Therapies TU Dresden, Germany

Publication history

  1. Received: July 6, 2020
  2. Accepted: October 13, 2020
  3. Accepted Manuscript published: October 14, 2020 (version 1)
  4. Version of Record published: November 16, 2020 (version 2)

Copyright

© 2020, Duerr et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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