Elastic Fiber Profiling in Lung Tissue Using Quantitative Imaging Analyses

Summary

Here, we present a protocol detailing tissue preparation for elastic fiber visualization and quantitative profiling in murine lung tissue. This method enables robust assessment of tissue architecture, spatial fiber organization, and extracellular matrix remodeling in physiological and pathological conditions.

Abstract

Remodeling of the pulmonary extracellular matrix (ECM) is a key feature of lung injury and disease. Elastic fibers are a central component of the lung ECM, providing resilience and recoil necessary for normal respiratory function, and their disruption contributes to impaired tissue mechanics. This protocol describes a robust, reproducible workflow for the preparation and analysis of murine lung tissue, with a focus on the visualization and quantitative profiling of elastic fibers. It outlines optimized tissue processing and histological staining methods to preserve and detect elastin structures. In addition, the protocol details an image analysis pipeline enabling quantitative assessment of elastic fiber abundance, organization, and spatial distribution within the lung. The workflow further incorporates stain vector estimation in QuPath, color deconvolution in Fiji/ImageJ, and TWOMBLI-based image analysis to enable quantitative assessment of elastic fiber abundance, branching, and network complexity. Strategies for image acquisition, region-of-interest selection, parameter optimization, and quality control are included to ensure reproducibility and minimize user-dependent bias. Using this workflow, we successfully identified alterations in elastic fiber organization and network architecture in the lungs of obese mice, demonstrating its applicability for detecting disease-associated ECM remodeling. Together, these approaches provide a comprehensive framework to investigate elastic fiber remodeling and its contribution to tissue architecture in physiological and pathological conditions. This method facilitates the study of fibroblast-driven structural changes and supports mechanistic insights into ECM alterations underlying lung disease.

Introduction

Elastic fibers are essential components of the pulmonary extracellular matrix (ECM), composed primarily of elastin and associated microfibrillar proteins that assemble into highly organized, resilient networks¹. In the lung, elastic fibers are arranged along alveolar septa and peribronchial structures, where they provide the elasticity and recoil required for normal respiratory mechanics^1,2. As one of the most abundant structural ECM components in tissues such as the lung, aorta, and uterus, its integrity is critical for maintaining tissue compliance^1,2. Disruption of elastic fiber organization has been observed in a range of pulmonary diseases, including fibrosis, emphysema, and chronic inflammatory conditions, where altered fiber architecture contributes to impaired lung function and mechanical properties^2,3.

The lung matrisome has been recently characterized, revealing core ECM proteins such as collagens and laminins, together with matrisome-associated components including cytokines, ECM regulators, and ECM-affiliated proteins (e.g., integrin ligands, galectins)^4,5. Beyond providing structural support, these components actively regulate tissue function and form specialized niches that support resident and migrating cells^6,7. Notably, recent studies have highlighted that metabolic conditions such as obesity are associated with significant alterations in elastic fiber organization, linking systemic metabolic dysregulation to changes in lung ECM architecture and fibroblast function³. These findings underscore the importance of understanding elastic fiber remodeling as a dynamic and disease-relevant process.

Conventional assessment of elastic fibers often relies on qualitative histological evaluation, semi-quantitative scoring systems, or measurements of elastin abundance, which provide limited information regarding fiber organization, connectivity, and network complexity^8,9. While these approaches are useful for detecting gross alterations in elastin content, they may not capture subtle architectural changes associated with tissue remodeling. Quantitative image-based analyses have the potential to overcome these limitations by enabling objective and reproducible characterization of elastic fiber networks and facilitating comparisons across experimental conditions and studies. To address this, the protocol described here enables integrated analysis of pulmonary ECM organization with a specific focus on elastic fiber architecture. In addition to established approaches for ECM characterization^4,5, this workflow incorporates Elastica staining and a TWOMBLI-based¹⁰ image analysis pipeline optimized for lung tissue architecture. By combining Elastica staining with quantitative network analysis, this workflow extends beyond conventional histological assessment and enables detailed characterization of fiber abundance, fragmentation, spatial organization, and structural complexity. The protocol is particularly well-suited for studies investigating fibrosis, emphysema, obesity-associated lung remodeling, inflammatory lung diseases, aging, and other conditions characterized by extracellular matrix remodeling in the lung.

Protocol

All mouse experiments described here were performed in accordance with the approval of the Animal Ethics Committee of the state of North Rhine-Westphalia, Germany (AZ: 81-02.04.2021.A221).

NOTE: Wear appropriate personal protective equipment (lab coat, gloves, eye protection) throughout the procedure.

1. Sampling and Preparation of murine lung tissue

NOTE: Mice were euthanized by isoflurane overdose according to institutional and governmental guidelines prior to tissue collection.

1. Carefully open the thoracic cavity with a midline incision, then cut the diaphragm along its attachment, and gently expose the lungs.
2. Remove the entire lung by excising the right and left lobes together, taking care to keep all lobes intact.
3. Place the lung lobes individually into a 5 mL tube containing 2 mL of 10% neutral buffered formalin and incubate for 15–20 h at 4 °C.
   NOTE: The fixation preserves tissue architecture for subsequent histological and elastic fiber analyses. Because the primary objective of this workflow was the visualization and quantitative assessment of elastic fiber organization rather than alveolar morphometric analyses, lungs were not inflation-fixed prior to tissue processing.
   CAUTION: Neutral buffered formalin contains formaldehyde, which is toxic, carcinogenic, and may cause skin, eye, and respiratory irritation. Handle formalin in a certified chemical fume hood while wearing appropriate personal protective equipment, including gloves, a lab coat, and safety glasses.
4. Embed the fixed lung tissue in paraffin using a standard histological embedding protocol¹¹.
   NOTE: Ensure complete dehydration and paraffin infiltration to maintain tissue morphology for subsequent staining. To minimize variability between samples and ensure compatibility across experimental conditions, always use the same lung lobes for each analysis. It is recommended to use the left lobe for histology.
5. Cut FFPE lung tissue into 5–7 µm-thick sections using a microtome and mount them on standard glass slides. Bake the slides at 60 °C for 1 h before proceeding with downstream applications. Slides can be stored at RT in a dust-free environment for several weeks or at 4 °C for long-term storage.
   NOTE: Avoid moisture exposure during storage, as humidity can cause paraffin oxidation and compromise staining quality. Store slides in a sealed plastic slide box with dessicant if long-term storage is required.
   Pause Point: Store slides at RT or 4 °C until staining.

2. Elastic fiber staining

1. Perform Elastica (resorcin-fuchsin) staining on 5–7 µm paraffin-embedded lung tissue sections to visualize elastic fibers within the alveolar and vascular structures.
   NOTE: Only stain as many slides as can be handled simultaneously to ensure consistent staining intensity and reproducibility across samples.
   1. Deparaffinate slides by immersing the slides in xylene (mixed isomers) two times for 5 min each.
      CAUTION: Xylene is toxic and flammable. Handle in a chemical fume hood and wear appropriate protective equipment.
   2. Rehydrate the slides in descending ethanol series until 80% ethanol.
      1. Incubate the slide for 3 min at RT in a staining chamber filled with 100% ethanol.
      2. Transfer the slide to fresh 100% ethanol and incubate 3 min at RT.
      3. Transfer the slide to 95% ethanol and incubate for 3 min at RT.
      4. Transfer the slide to 80% ethanol and incubate for 3 min at RT.
   3. Transfer the slide into a new staining chamber containing Resorcin-Fuchsin solution and incubate for 20–30 min at RT.
      NOTE: Resorcin-Fuchsin staining solution can be used several times and is stable at RT.
      CAUTION: Resorcin-fuchsin staining solution contains potentially hazardous chemicals, including concentrated acids and dyes, which may cause skin and eye irritation. Handle the solution in a well-ventilated area or fume hood and wear appropriate personal protective equipment according to institutional safety guidelines.
   4. Place the slide in a staining chamber under running distilled water and wash for 1 min.
   5. Differentiate in 95–96% ethanol: 15–60 s, monitor under the microscope.
      NOTE: Stop when elastic fibers remain deep purple-violet, and the background is adequately cleared. Stained elastic fibers appear dark purple. Collagen fibers remain unstained and typically appear pale. Overstaining can increase background; adjust incubation time depending on tissue thickness and fixation.
   6. Stop the differentiation by transferring the slide immediately into bidistilled water and rinsing for 30 s. Optional: Counterstaining can be performed if needed for additional lung evaluation, but it may reduce fiber segmentation efficiency. We recommend performing H&E staining to evaluate the lung on adjacent cuts.
   7. Dehydrate the slides sequentially in 95% ethanol for 1–2 min, followed by 100% ethanol twice for 1–2 min each to remove residual water.
   8. Clear the sections in xylene 2 times for 3 min until the tissue appears optically transparent.
   9. Mount coverslips using a xylene-compatible permanent synthetic resin and allow slides to dry flat at RT.
      NOTE: Complete dehydration and clearing are essential to prevent cloudiness and ensure long-term preservation of the stained sections.
      Pause Point: Store stained mounted slides at RT until imaging.

3. Elastic fiber imaging and network analysis

1. Image acquisition
   1. Acquire high-resolution brightfield images of lung tissue sections using a slide scanner with a 40× objective. Ensure consistent exposure and white balance settings across all samples.
      NOTE: Images were acquired using identical scanner settings throughout the study, including magnification, spatial resolution (0.25 µm/pixel), illumination, and exposure.
   2. Draw a region of interest (ROI) encompassing the entire lung tissue section to ensure complete coverage during scanning.
   3. Define a background reference area without tissue to measure and subtract background intensity during image analysis.
   4. Use the scanner’s automatic focus detection to assign an optimal number of focal points across the tissue, ensuring uniform sharpness and image quality.
   5. Acquire the image using uniform illumination and exposure settings.
      NOTE: If automatic focusing fails in dense or uneven regions, use manual fine focus adjustment to ensure that all tissue structures, including alveolar and vascular areas, are sharply captured.
2. Image Deconvolution: stain vector estimation in QuPath¹². Stain vectors represent the specific RGB absorbance values of each histological dye, allowing separation of overlapping stains during color deconvolution.
   NOTE: In this protocol, stain vectors were defined in QuPath, as predefined stain vectors for resorcin-fuchsin are not available in Fiji.
   1. Open the image in QuPath and select the brightfield image type.
      NOTE: To minimize bias, calculate stain vectors using representative images from the dataset and apply the same values to all images.
   2. Draw a region of interest (ROI) that contains predominantly elastic fibers to define the first stain component.
   3. In the Image tab, navigate to the Stain vector sections.
   4. Click Stain 1, then set stain vector from ROI to define the color vector for elastic fibers (typically purple-violet).
   5. Select an ROI containing non-elastic tissue (e.g., background or collagen) and click stain 2, then set the stain vector from the ROI to define the secondary color component.
   6. QuPath automatically calculates Stain 3, corresponding to the background.
   7. An example for vectors calculated and applied in Fiji: Resorcin-Fuchsin R:0.6201225, G:0.6541292, B:0.43308553; Unspecific signal R:0.31585205; G:0.815618; B:0.48477295; Background R:-0.105005614, G:-0.4780256, B:0.87204665.
      NOTE: These values were stained for images stained only with resorcin-fuchsin. If a counterstain is present, new stain vectors have to be calculated.
   8. Save these as custom stain vectors and apply them for consistent color deconvolution across all samples.
      NOTE: Defining custom stain vectors from representative regions improves color deconvolution accuracy, particularly for resorcin-fuchsin staining, where elastin and background hues may vary across batches.
3. Image Color Deconvolution in Fiji^13,14.
   1. Open Fiji (ImageJ) and load the brightfield image of the stained tissue section.
   2. Optional: Transform the image to RGB format (Image → Type → RGB Color) only if the image is not already in RGB (e.g., 8-bit images). This step opens a new image window; save it to preserve the original raw data.
   3. Perform color deconvolution by selecting Image > Color > Color Deconvolution > User Values.
   4. Enter the custom stain vector previously obtained from QuPath.
   5. Run the plugin to generate three separate grayscale images corresponding to elastic fibers, tissue excluding elastic fibers, and background.
   6. Use images directly for TWOMBLI without additional preprocessing. TWOMBLI requires sharp images with sufficient resolution; smoothing (e.g., Gaussian blur) should be avoided to preserve structural details.
      NOTE: Ensure that the regions selected in QuPath for vector estimation are representative of typical staining, as these custom vectors will be applied uniformly across all images for consistent comparison.
   7. Save the elastic fiber channel as a TIFF file for downstream quantification and analysis.
4. ROI Selection for Fiber Analysis
   1. For each lung section, select five unbiased square ROIs in the alveolar region and five ROIs in the peribronchiolar region (100 µm x 100 µm each).
   2. Duplicate each ROI on the raw image (Image > Duplicate) to preserve the original data.
   3. Duplicate the same ROIs on the deconvoluted elastic fiber analysis.
   4. Save all duplicated ROIs as individual TIFF files for downstream analysis.
      NOTE: Regions containing tissue folds, sectioning artifacts, tears, large blood vessels, or poorly preserved alveolar architecture were excluded from analysis. Only the deconvolved elastic fiber ROIs were used as input for TWOMBLI analysis, whereas corresponding raw image duplicates were retained as reference files for quality control and visual inspection. Regions of interest (ROIs) should be selected using systematic random sampling to ensure unbiased representation of the tissue. To further minimize operator bias, an automated ROI selection script compatible with QuPath is provided in Supplementary File 1 and Supplementary File 2, and its implementation is described in Section 3.5.
5. Applying the automated ROI selection in QuPath using an automated ROI selection script.
   1. Open the image in QuPath and set the image type to Brightfield.
   2. From the Tools menu, select the desired annotation tool to create the initial annotations. Square annotations are recommended.
   3. Draw square annotations within the lung tissue. Each annotation should encompass only one region of interest (ROI) for analysis, either an alveolar region or a peribronchial area.
   4. Once the annotations have been created, navigate to Annotations and select Lock annotations to prevent accidental movement or modification.
   5. Add the classes “alveoli” and “peribronchial area” to the class list by clicking the “+” button next to Class list in the annotation panel.
   6. Assign each annotation to the appropriate class. Select an annotation, choose the corresponding class, and click Set selected. The annotation color will change, and the assigned class ("alveoli" or "peribronchial area") will be displayed in the annotation properties panel.
   7. Open the corresponding script and execute it. Upon completion, a message will indicate that the ROIs have been successfully generated. The newly created ROIs will appear in the annotation hierarchy as child annotations of the original class-defining annotation.
   8. To export the ROIs, select them and navigate to File → Export Images → Original Pixels. Save the exported ROIs as TIFF files.
      NOTE: The scripts are designed to recognize only the class names "alveoli" and "peribronchial area". These names must be entered exactly as specified. By default, the scripts randomly generate five non-overlapping square ROIs measuring 100 µm × 100 µm within each annotation. Candidate ROIs are accepted only if their corners and center are fully contained within the annotation boundaries, ensuring complete inclusion within the selected region. Both the number and dimensions of the ROIs can be modified within the script to accommodate specific experimental requirements. At least three biological replicates and a minimum of five ROIs/sample are recommended for analyses.
6. TWOMBLI analysis
   1. Save all ROI Images of elastic fibers in a dedicated folder for analysis.
   2. Create two subfolders within the working directory: a. Mask folder – for binary masks generated by TWOMBLI. b. HDM folder – for High-Density Matrix (HDM) images generated by TWOMBLI.
   3. Run the TWOMBLI plugin: Open Fiji and launch TWOMBLI (Plugins -> Macros -> Run).
   4. Select the appropriate analysis parameter file. The following TWOMBLI parameters were used for elastic fiber analysis, listed in Table 1.
      NOTE: These parameters may vary depending on staining intensity. Parameters were empirically optimized using multiple representative images from lung tissue sections stained under standardized conditions. Optimization was performed by comparing the generated masks with the original staining pattern and selecting the settings providing the best overlap between the segmented mask and the underlying elastic fiber network. Once established, the same parameters were applied to all images within the dataset.
   5. Choose the “dark fibers over light background” option.
   6. Generate and save binary masks in the Mask folder.
   7. Generate and save binary images in the HDM folder.
   8. Extract quantitative network parameters such as fiber length, branch points, curvature, density, and alignment.
      NOTE: TWOMBLI analysis requires sharp, high-resolution images (>1 pixel/µm). Poor color deconvolution, out-of-focus regions, or image compression can compromise the accuracy of fiber network quantification.
7. Quantification and Statistical Analysis
   1. Export TWOMBLI network parameters as a CSV file.
   2. Import the raw CSV data into GraphPad Prism for statistical analysis and visualization.
      NOTE: The following parameter is recommended: Mask Total Length, which reflects the total amount of elastic fibers within the ROI, allowing quantification of fiber abundance and loss. Fiber Endpoints indicates the degree of fiber fragmentation; an increased number of endpoints suggests disrupted or broken fibers. Fiber Branchpoints measure how interconnected fibers are, reflecting the structural complexity and integrity of the ECM network. A higher number of branch points indicates a more complex and interwoven network. Lacunarity quantifies the homogeneity of fiber distribution across the ROI. Low lacunarity indicates a uniform network with similarly sized gaps, whereas high lacunarity reflects a heterogeneous network with variable gap sizes describing a more complex structure. Box Counting-Fractal Dimension captures the complexity of fiber architecture. Higher fractal dimension values indicate more intricate and detailed networks, while lower values reflect simpler or disrupted structures.
   3. Generate column graphs showing mean ± SEM for each quantified network parameter (e.g., fiber length, branching, curvature, and density).
   4. Test data normality using the Shapiro-Wilk test. For parametric data, apply a two-tailed unpaired Student’s t-test. For non-parametric data, apply a Mann-Whitney U test. For comparisons involving more than two experimental groups, a one-way analysis of variance (ANOVA) followed by an appropriate post hoc multiple-comparison test, or a Kruskal–Wallis test for non-parametric data, should be applied.
      NOTE: Keep all raw CSV files unaltered to ensure reproducibility and data traceability. Report the number of ROIs and biological replicates analyzed for each condition in the figure legends.

Results

This protocol provides quantitative parameters of the elastic fiber network across different lung regions and experimental conditions. TWOMBLI analysis, as previously described, yields parameters such as fiber length, branching density, lacunarity, and fractal dimension for extracellular matrix assessment¹⁰. Building upon this framework, our approach enables a comprehensive characterization of elastic fiber organization, allowing assessment of changes in fiber abundance, connectivity, uniformity, and structural complexity in both physiological and pathological lung tissues.

In healthy lung tissue, elastic fibers form long, continuous, and well-aligned networks along the alveolar septa and peribronchial structures. In contrast, pathological or metabolically altered lungs, such as those from obese mice, often exhibit increased branching and greater network complexity, reflecting alterations in elastic fiber organization³. Representative outputs, including binary masks and quantitative parameters, are depicted in Figure 1A, B. These outputs can be integrated with histological and molecular analyses to assess ECM integrity and the extent of remodeling³. Lacunarity and gap filling are exemplified in healthy lung tissue to visualize differences in ECM organization (Figure 1C). In combination with other quantitative metrics, these parameters capture network complexity and reveal changes in tissue architecture driven by ECM reorganization, providing insights into distinct patterns of structural remodeling within the elastic fiber network.

Accurate interpretation of these quantitative outputs critically depends on high-quality image acquisition and processing. Suboptimal imaging or processing conditions, including poor focus, inconsistent illumination, or inaccurate stain vector estimation, can result in fragmented or noisy masks and, consequently, unreliable quantitative outputs (Figure 1D). Therefore, careful visual inspection of raw images and segmentation results is essential to ensure data quality prior to interpretation. Suboptimal masks can typically be recognized by excessive fragmentation of continuous fibers, discontinuous or incomplete segmentation, incorporation of background noise as fiber structures, or poor overlap between the original staining pattern and the generated mask, indicating that image acquisition, stain vector estimation, or segmentation parameters should be re-evaluated. To illustrate these features, sections with counterstaining were intentionally analyzed, which interferes with elastic fiber identification and results in suboptimal segmentation. An example of such a low-quality mask is shown in Figure 1E. To further assess the robustness and reproducibility of the workflow, image datasets were independently analyzed by two investigators using the same optimized analysis parameters (Figure 2A). In addition, samples derived from independent animals were included to evaluate biological variability (Figure 2B). Comparable quantitative outputs were obtained across operators and biological replicates, supporting the reproducibility and user-independence of the proposed image analysis pipeline. Together, these analyses demonstrate that the workflow enables robust assessment of elastic fiber organization and facilitates comparisons between physiological and pathological conditions.

Figure 1. Elastica Staining Analysis Workflow. (A) Representative raw image, isolated elastic fiber signal obtained by image deconvolution, binary mask generated using TWOMBLI, and overlay of the original elastic fibers with the corresponding mask, demonstrating accurate segmentation. (B) Representative quantitative parameters generated by TWOMBLI analysis. (C) Examples of lacunarity and gap-filling analyses in healthy murine lung tissue to illustrate differences in extracellular matrix organization. (D) Illustration of fragmented and noisy masks resulting from suboptimal image acquisition or image processing. (E) Tissue section counterstained with hematoxylin and eosin, demonstrating impaired elastic fiber identification and suboptimal mask generation. Areas showing poor correspondence between the original staining pattern and the generated mask are indicated. Scale bar A, D = 100 µm, E = 50 µm. Please click here to view a larger version of this figure.

Figure 2. Reproducibility and robustness of the elastic fiber analysis workflow. (A) Image datasets were independently analyzed by two investigators using TWOMBLI analysis parameters to assess inter-operator reproducibility. Quantitative TWOMBLI-derived metrics are shown. A and B indicate the two independent operators. (B) Samples derived from independent animals were analyzed to evaluate biological variability. Quantitative TWOMBLI-derived metrics are shown, demonstrating comparable outputs across biological replicates and supporting the robustness of the analysis pipeline. M1, M2, and M3 indicate individual healthy animals. n = 3, Statistical analysis was performed using one-way ANOVA followed by Dunnett's multiple comparisons test. Please click here to view a larger version of this figure.

Parameters	Values
Contrast saturation	0.75
Minimum line width	5
Maximum line width	10
Minimun curvature window	20
Maximun curvature window	80
Minimun branch length	10
Maximun HDM display	175
Minimun gap diameter	0

Table 1: Summary of TWOMBLI-derived quantitative parameters. Overview of the parameters generated by TWOMBLI and their interpretation for the assessment of elastic fiber architecture in lung tissue. Metrics include measures of fiber abundance, branching, alignment, connectivity, and network organization.

Supplementary File 1: Unbiased ROI alveoli Please click here to download this file.

Supplementary File 2: Unbiased ROI peribronchial area Please click here to download this file.

Discussion

This protocol provides a robust, reproducible workflow for investigating murine pulmonary extracellular matrix (ECM) architecture, with a particular focus on fibroblast-driven elastic fiber remodeling. Elastic fibers are essential for maintaining lung compliance and recoil, and their disruption is a hallmark of multiple pulmonary diseases, including fibrosis, lung injury, aging, and metabolic disorders². Despite their central functional role, quantitative assessment of elastic fiber organization and assembly has remained limited. The workflow addresses this gap by enabling systematic and quantitative characterization of elastic fiber network architecture.

A major strength of this method lies in integrating a quantitative imaging workflow with structural histology analysis. While conventional approaches often assess ECM composition or histological features separately, this protocol enables objective quantification of elastic fiber organization, including fiber length, branching, and network complexity. This allows direct comparison between physiological and pathological conditions and provides insights into fibroblast-mediated ECM remodeling³.

However, several technical aspects are critical for ensuring experimental consistency and data quality. Resorcin–fuchsin staining is sensitive to variables such as fixation time, section thickness, and reagent consistency, making standardization essential for reproducibility. Quantitative outputs from TWOMBLI depend on image quality, resolution, and consistent region-of-interest selection, and must therefore be interpreted alongside histological evaluation and validated across multiple samples. In addition, fiber density and morphology differ between alveolar and peribronchial regions, requiring region-specific optimization. This approach captures the global organization of the elastic fiber network in two dimensions and does not resolve ultrastructural features, which require higher-resolution techniques such as electron microscopy. Three-dimensional imaging modalities, including confocal or multiphoton microscopy, may further enhance the analysis of fiber geometry and connectivity.

Several steps within the workflow are particularly critical for ensuring reliable and reproducible results. Tissue fixation, sectioning quality, and consistent resorcin-fuchsin staining substantially influence elastic fiber visualization and should therefore be carefully standardized.  Poor staining intensity, uneven staining, or high background can result from variable reagent quality, inconsistent staining conditions, or inadequate tissue preservation. In addition, inclusion of healthy and diseased lung tissues, technical staining controls, and reference samples processed across experimental batches is recommended to monitor staining consistency and analytical robustness. Tissues rich in elastic fibers may further serve as positive controls to validate image acquisition and segmentation procedures. Accurate stain vector estimation during image deconvolution is another essential step, as inappropriate vector selection can lead to incomplete fiber extraction and fragmented masks. Depending on the experimental question, the workflow can be modified by adjusting ROI size and number, adapting image processing parameters, or integrating three-dimensional imaging approaches to further investigate elastic fiber organization.

Overall, this method represents a versatile platform to study ECM remodeling in both physiological and pathological contexts. As elastic fiber integrity is directly linked to lung compliance and mechanical function, this approach is particularly important for understanding how structural changes in the ECM translate into alterations in pulmonary function. It is well-suited for investigating fibroblast-driven structural changes and provides new opportunities to better understand ECM remodeling and its contribution to disease progression and tissue function. The approach can also be extended to other tissues, including cardiovascular tissues, skin, uterus, connective tissue disorders, fibrotic organs, and human specimens, or combined with advanced spatial and high-resolution imaging techniques to further broaden its applicability.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Axio CS2 Slide Scanner	Carl Zeiss
Coplin Glass Staining jar	Sigma-Aldrich	S6016
DPX Mountant	Sigma-Aldrich	6522
Ethanol (100%) for histology	Carl Roth	9065.4
Fiji		https://imagej.net/software/fiji/	version 1.54t
Formalin solution, neutral buffered 10%	Sigma	HT501128
HISTOSETTE I Tissue Processing/Embedding Cassettes	Sigma-Aldrich	H0542
microtome	Thermo Fischer Scietific	HM355S
Micrsocope Slides	VWR	631-0108
Mouse: C57BL/6J	The Jackson Laboratory	RRID: IMSR_JAX:000664	Strain #:000664
paraffin embedding system	Epredia HistoStar embedding module
Prism		https://www.graphpad.com	version 11
Qupath		https://qupath.github.io	version 0.7.0
Resorcin-Fuchsin	Waldeck	2.00E-30
TWOMBLI plugin		https://github.com/wershofe/TWOMBLI
Xylene	Applichem GmBH	14020172