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Stain-Free Tissue Imaging: Advancing Fibrosis Research

Stain-Free Tissue Imaging: Advancing Fibrosis Research


Advanced imaging techniques play a crucial role in furthering our understanding of fibrotic diseases. Among these, stain-free tissue imaging is emerging as a valuable tool in fibrosis research. This category of imaging methods, including techniques like Second Harmonic Generation (SHG) microscopy, offers an alternative approach to visualizing tissue structures without using traditional chemical stains.

For researchers and pathologists studying fibrosis, Second Harmonic Generation microscopy presents new possibilities for analyzing tissue samples. By providing detailed visualization of collagen structures – a key component in fibrotic tissues – these methods offer potential advantages in quantitative analysis and standardization of fibrosis assessment1.

In this article, you will learn more about stain-free imaging, particularly

  1. what stain-free imaging is, and techniques like second harmonic generation
  2. how it is different from traditional staining methods, and its advantages
  3. Its applications in fibrosis research
  4. emerging trends that may shape the future of this field.

What is Stain-Free Tissue Imaging?

Stain-free tissue imaging refers to a category of imaging methods that allow for the visualization and analysis of tissue samples without using traditional chemical stains. Second Harmonic Generation (SHG) microscopy is a prominent technique in this category.

SHG is an optical process that occurs in certain materials, including collagen, a key structural protein involved in fibrosis. In SHG microscopy, high-intensity light interacts with non-centrosymmetric structures in the tissue, generating light at exactly twice the incident frequency. This phenomenon allows for the visualization of collagen fibers without needing exogenous labels or stains.

Techniques like SHG microscopy offer an alternative to traditional staining methods for tissue analysis. Traditional tissue imaging relies on chemical staining to differentiate between various tissue components. Standard staining techniques used in fibrosis assessment include Hematoxylin and Eosin (H&E), Masson’s Trichrome, and Picosirius Red staining. These methods involve multiple steps, including fixation, dehydration, staining, and mounting.

How Does Stain-Free Tissue Imaging Differ From Traditional Staining? 

Accurate tissue imaging is essential in studying, diagnosing, and monitoring fibrosis. The choice between conventional staining and stain-free methods often depends on the specific research question, type of tissue, and available resources.

Aspect Traditional Staining Methods Stain-Free Imaging Techniques
Sample Preparation Multiple steps: fixation, dehydration, staining, and mounting Minimal sample preparation but may require specialized tissue handling
Artifacts Potential staining artifacts, which are well understood and can be controlled Reduced chemical artifacts but may introduce technique-specific artifacts that are less well-characterized
Reproducibility Prone to inter- and intra-observer variability and operator differences Potentially consistent results, but dependent on equipment calibration.
Visualization Differentiation of tissue components based on well-understood staining patterns Visualizes specific structures (e.g., collagen) based on intrinsic optical properties
Quantification Ordinal scoring systems (e.g., NASH-CRN, Ishak, METAVIR) widely accepted in clinical practice Digital image analysis allows for detailed measurements, but clinical validation is still ongoing
Data Output Produces familiar images for pathologists; easily interpretable in clinical settings Produces high-quality, digitized images suitable for computational analysis but may require specialized training
Clinical Validation Extensively validated and widely accepted in clinical practice Promising results in research settings, but clinical validation is still in progress

The use of stain-free imaging techniques in digital pathology can offer further advantages.

  1. Digital Image Analysis: Stain-free techniques produce high-quality, digitized images that can be readily analyzed using advanced software tools. This digital format facilitates the application of sophisticated image analysis algorithms, which can provide detailed quantitative data on collagen structures and other tissue features2.
  2. Standardized Assessment: The digital nature of stain-free imaging, combined with automated analysis tools, may help standardize fibrosis assessment across different samples and studies, reducing inter-observer variability and improving the consistency of fibrosis scoring3.
  3. Preservation of Tissue: Since stain-free methods don’t require chemical staining, the original tissue sample is preserved in its native state. This can be particularly valuable when tissue samples are limited or further analyses (such as molecular studies) are to be performed on the same sample.

Applications of Stain-Free Tissue Imaging in Fibrosis Research

  1. Characterization of Collagen Microstructure. Stain-free imaging allows researchers to visualize and analyze the detailed microstructure of collagen fibers in fibrotic tissues. This can provide insights into how the extracellular matrix changes during fibrosis progression4. Detailed structural information can help in understanding the mechanisms of fibrosis development and regression at a molecular level.

For example, researchers can study specific fibrosis features like:

  • Collagen fiber thickness and density
  • Fiber orientation and organization
  • Changes in collagen crosslinking
  1. Quantitative Assessment of Fibrosis in Animal Models. In preclinical research, stain-free imaging techniques quantitatively assess fibrosis in animal models of various diseases, allowing for:
  • More precise measurement of fibrosis progression over time
  • Evaluation of anti-fibrotic therapies in development
  • Comparison of fibrosis patterns across different animal models

The ability to obtain quantitative data on fibrosis severity and progression can enhance the translational value of animal studies.

  1. Validation of Novel Biomarkers. Stain-free imaging can be used to validate potential biomarkers of fibrosis. By correlating the expression or presence of candidate biomarkers with quantitative measures of collagen deposition and organization, researchers can:
  • Assess the sensitivity and specificity of new biomarkers
  • Understand how biomarker levels relate to structural changes in the tissue
  • Develop more accurate non-invasive methods for fibrosis assessment

These research applications highlight the versatility of stain-free imaging techniques in advancing our understanding of fibrosis. By providing detailed, quantitative information on tissue structure and composition, these methods contribute to new insights into fibrosis research and support the development of novel therapeutic strategies.

Rezdiffra Case Study: A Breakthrough in MASH Treatment

SHG microscopy and AI-based analysis, qFibrosis, was incorporated in the Phase 2b and Phase 3 MASH trials and provided a quantitative approach to assessing fibrosis improvement by analyzing fine architectural changes in collagen fibers.

In Madrigal’s Phase IIb MASH trial, qFibrosis revealed that baseline F3 patients were the best responders, with approximately half showing ≥1-point improvement in fibrosis. This data informed Madrigal’s Phase 3 trial design, which included more F3 patients and demonstrated significant improvements in primary endpoints: MASH resolution and fibrosis stage improvement. The AI-based measurements confirmed reductions in fibrosis in Rezdiffra-treated groups, highlighting the efficacy of Madrigal’s treatment.

Future Trends in Tissue Imaging 

AI-Powered Image Analysis for Patient Outcome Prediction

While the integration of AI with digital pathology is already well-established, a cutting-edge trend is using AI to analyze tissue images in conjunction with patient data for outcome prediction. This approach holds significant promise for fibrosis research and patient care:

  • Longitudinal Studies: AI can track subtle morphological and architectural changes in tissue structure over time and identify early disease progression indicators that might not be apparent to human observers.
  • Multimodal Data Integration: AI algorithms can now integrate stain-free tissue imaging data with patients’ information, such as clinical history, genetic data, and blood biomarkers, for more comprehensive analysis and potentially more accurate predictions5.
  • Predictive Modeling: By analyzing patterns in tissue structure alongside patient data, AI models can predict treatment response, disease progression, and long-term patient outcomes, where the rate of progression and response to treatment can vary significantly between patients.

AI-Driven Personalized Medicine in Fibrosis Treatment

The application of AI in analyzing patient biopsies is opening new avenues for personalized medicine in fibrosis treatment. This trend focuses on using AI to identify specific characteristics in patient samples that indicate the likelihood of response to particular therapies:

  • Biomarker Discovery: AI analysis of tissue images may uncover new biomarkers that may unravel information on disease progression, potentially leading to the development of more targeted therapies.
  • Treatment Response Prediction: AI algorithms can analyze stain-free images of patient biopsies to identify subtle features or patterns that correlate with positive responses to therapies. This can help trial enrollment and provide the most effective treatment for each patient.
  • Designing of Clinical Trials: This AI-driven approach could inform the design of clinical trials, where critical information presented may be considered for trial powering and duration decisions.

These emerging trends highlight the potential for AI to leverage the detailed structural information provided by stain-free imaging techniques, transforming how we approach fibrosis research and patient care.


  1. Sanyal AJ, Jha P, Kleiner DE. Digital pathology for nonalcoholic steatohepatitis assessment. Nat Rev Gastroenterol Hepatol. 2024;21:57-69.
  2. Ratziu V, Kleiner DE, Bedossa P, et al. Digital pathology and artificial intelligence in non-alcoholic steatohepatitis. J Hepatol. 2023. doi:10.1016/j.jhep.2023.10.015
  3. Liu F, Goh GB, Tiniakos D, et al. qFIBS: Automated technique for quantitative evaluation in nonalcoholic steatohepatitis. Hepatology. 2020;71(6):1953-1966.
  4. Ng N, Xiao Y, Leow WQ, et al. Second-harmonic generated quantifiable fibrosis parameters in nonalcoholic fatty liver disease. Clin Pathol. 2023;16:2632010X231162317.
  5. Kendall TJ, Duff CM, Thomson SJ, et al. Integrated gene-to-outcome database for metabolic dysfunction-associated steatotic liver disease. Nat Commun. 2023;14(1):3918.