Evolution of the liver biopsy and its future

Abstract

Liver biopsies are commonly used to evaluate a wide variety of medical disorders, including neoplasms and post-transplant complications. However, its use is being impacted by improved clinical diagnosis of disorders, and non-invasive methods for evaluating liver tissue and as a result the indications of a liver biopsy have undergone major changes in the last decade. The evolution of highly effective treatments for some of the common indications for liver biopsy in the last decade (e.g., viral hepatitis B and C) has led to a decline in the number of liver biopsies in recent years. At the same time, the emergence of better technologies for histologic evaluation, tissue content analysis and genomics are among the many new and exciting developments in the field that hold great promise for the future and are going to shape the indications for a liver biopsy in the future. Recent advances in slide scanners now allow creation of "digital/virtual" slides that have image of the entire tissue section present in a slide [whole slide imaging (WSI)]. WSI can now be done very rapidly and at very high resolution, allowing its use in routine clinical practice. In addition, a variety of technologies have been developed in recent years that use different light sources and/or microscopes allowing visualization of tissues in a completely different way. One such technique that is applicable to liver specimens combines multiphoton microscopy (MPM) with advanced clearing and fluorescent stains known as Clearing Histology with MultiPhoton Microscopy (CHiMP). Although it has not yet been extensively validated, the technique has the potential to decrease inefficiency, reduce artifacts, and increase data while being readily integrable into clinical workflows. Another technology that can provide rapid and in-depth characterization of thousands of molecules in a tissue sample, including liver tissues, is matrix assisted laser desorption/ionization (MALDI) mass spectrometry. MALDI has already been applied in a clinical research setting with promising diagnostic and prognostic capabilities, as well as being able to elucidate mechanisms of liver diseases that may be targeted for the development of new therapies. The logical next step in huge data sets obtained from such advanced analysis of liver tissues is the application of machine learning (ML) algorithms and application of artificial intelligence (AI), for automated generation of diagnoses and prognoses. This review discusses the evolving role of liver biopsies in clinical practice over the decades, and describes newer technologies that are likely to have a significant impact on how they will be used in the future.

Keywords: Clearing Histology with MultiPhoton Microscopy (CHiMP); Liver biopsy; matrix assisted laser desorption imaging (MALDI); whole slide imaging (WSI).

Figure 1

Fibrosis in liver represents an important feature of liver injury and characterization of fibrosis forms an important aspect of evaluation of hepatic disorders. Representations of liver fibrosis showing key features suggesting “progression” and “regression”. A case of hepatitis C cirrhosis (A) with predominantly progressive pattern of fibrosis characterized by many broad fibrous septa. Compare this with predominantly regressive pattern of fibrosis (B) post therapy with sustained viral response in a case of hepatitis C cirrhosis showing many thin fibrous septa (A&B, 40×, Trichrome stain). Higher power images showing increased thickness, more inflammation and increased cellularity in progressive septum (C) compared to those undergoing regression characterized by relatively hypocellular, less vascular and thin septum (D) (C&D, 200×, H&E stain).

Figure 2

Second harmonic generation for liver collagen assessment using CHiMP multiphoton microscopy in a human cirrhotic liver. Red—Autofluorescence. Blue—SHG. Scale bar =0.5 mm. CHiMP, Clearing Histology with MultiPhoton Microscopy.

Figure 3

Multiphoton microscopy image of normal human liver tissue without need for wax-embedding or physical sectioning. Image collected by CHiMP processing and imaging scope technique. Fluorescence image has been converted to a pseudo-HE coloration. Scale bar =100 µm. CHiMP, Clearing Histology with MultiPhoton Microscopy.

Figure 4

A summary of the MALDI workflow for both rapid diagnostics and tissue imaging. Final data (both tissue images and spectra) are actual experimental data from mouse liver. *Note that for formalin-fixed paraffin-embedded (FFPE) tissues other steps may be necessary including deparaffinization and antigen retrieval or direct application of trypsin digestion. MALDI, matrix assisted laser desorption imaging.

Figure 5

Higher magnification of an digitally scanned liver biopsy (show in the inset) in a case of autoimmune hepatitis with identification of plasma cells (purple) and hepatocytes (yellow) following training of a digital image analysis algorithm that can be used for artificial intelligence (AI) and machine learning (original H&E stained slide; 200×).