This publication was supported by the fifth national traditional Chinese medicine clinical excellent talents research program organized by the National Administration of Traditional Chinese Medicine. The official network link is &#39;http://www.natcm.gov.cn/renjiaosi/zhengcewenjian/2021-11-04/23082.html. &#39;

This study utilized 3D-digital LSD modeling to reconstruct the liver of a typical patient with clinically confirmed hepatic fibrosis. The patient was recruited from a well-known liver disease treatment institution, &#34;You An Hospital&#34; in Beijing, China, and underwent routine upper abdominal magnetic resonance imaging (MRI) and MRE imaging after providing consent. The patient was chosen as the case study for this research method due to the confirmation of hepatic fibrosis staging through pathological examination and the absence of obvious clinical symptoms, which emphasizes the applicability and clinical value of this research in diagnosing early-stage hepatic fibrosis patients. This paper also provides a quantitative comparison between the liver of this patient and a healthy liver. The software tools used in this study are listed in the Table of Materials.
1. Data collection and preparation
NOTE: The parameter difference is not sensitive to the research method.
<ol>
	<li>MRI scanning strategies 
	NOTE: This study utilized actual DICOM data obtained from clinical imaging using a magnetic resonance imaging (MRI) device manufactured by GE. The content of the data includes IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation) water-fat separation imaging and magnetic resonance elastography (MRE) imaging.
	<ol>
		<li>Ensure that the IDEAL data have a horizontal resolution of 256 pixels by 256 pixels, with a pixel spacing of 1.5625 mm and a slice thickness of 10 mm. 
		NOTE: The scanning strategy could be further optimized, but the methodology employed in this study is applicable to higher-precision medical imaging.</li>
	</ol>
	</li>
	<li>Rename the folder of every sequence. 
	NOTE: As the DICOM data exported from the equipment does not explicitly provide sequence names, during the preprocessing stage, it is necessary to add explicit names for each sequence to facilitate subsequent analysis and processing.
	<ol>
		<li>Copy all DICOM data to a customized working directory.</li>
		<li>Navigate to the directory containing the data in MATLAB&#39;s working directory.</li>
		<li>Execute the Description_Name function to add descriptive names to the folders for each sequence.</li>
		<li>See Figure 1 for a comparison before and after renaming. Add a Description Name to each image sequence folder to facilitate the identification of the necessary image sequences for various analytical purposes.</li>
	</ol>
	</li>
	<li>Quickly check images of IDEAL.
	<ol>
		<li>Change the directory of different phases&#39; folders, including the in-phase, out-phase, water, and fat phases, which were stored in separate folders for imaging using IDEAL.</li>
		<li>Execute the Slice_View function to view the impact sequences for each phase.</li>
		<li>See Figure 2 for an image of the interactive graphic user interface (GUI) for the MRI-IDEAL sequence. Use the scroll bar at the bottom of the GUI to quickly browse through the different sequences.</li>
		<li>Use the MRI-IDEAL out-phase sequence as the type of MRI sequence for providing clearer descriptions of liver tissue boundaries. 
		&#8203;NOTE: In the following operations, the focus will be on using IDEAL&#39;s out-phase sequence to delineate the 3D region of the liver.</li>
	</ol>
	</li>
</ol>
2. Extract the 3D region of the liver
NOTE: The individual voxels in the 3D region of the liver serve as spatial carriers for LSD, with the stiffness value of each voxel being derived from MRE. Extracting the 3D region of the liver tissue is a necessary step before fusion. While deep learning can be used to accomplish this task more efficiently, it is not the focus of this study. Therefore, mature software tools (e.g., MIMICS) are still used here to extract the 3D region of the liver tissue.
<ol>
	<li>To initiate the MIMICS software, select New Project and in the ensuing dialog box, navigate to the folder containing the IDEAL out-phase images. Proceed by clicking on NEXT | the Convert button, thereby gaining entry into the sequence-editing state.</li>
	<li>To create an empty Mask, click on the New button in the MASK dialog box located on the right-hand side and select the maximum threshold.</li>
	<li>To delimit the area of the liver in all horizontal views, utilize the Edit Masks tool located beneath the Segment label.</li>
	<li>To generate the 3D spatial part of the liver, select the liver mask that has been delineated and click on the Calculate Part from Mask button. The extracted 3D region of the liver is shown in Figure 3.</li>
	<li>Click on File | Export | select the Dicom command. In the popup dialog box, choose the liver mask, set the file path and files&#39; names, and click the OK button to complete the export of the 3D region of the liver to the specified DICOM files.</li>
</ol>
3. The Liver stiffness map sequence
NOTE: The MRE stiffness range in patients with early fibrosis is typically below 8 kpa. To view this, the sequence image labeled &#39;SE27_ST8K_(Pa)&#39; should be selected.
<ol>
	<li>Change the directory to the folder of &#39;SE27_ST8K_(Pa)&#39;, which contains the liver stiffness map sequence.</li>
	<li>To browse through each stiffness map, execute the MRE_show function in Matlab&#39;s workspace, with the function&#39;s argument being the filename located in the specified path.</li>
	<li>The liver stiffness map shown in Figure 4 is an RGB true-color image, with a data structure of 512 pixels by 512 pixels by 3 matrix, where each pixel point has three values representing the three primary colors, RBG. Observe the color bar on the left that displays the corresponding stiffness values of different colored pixels. Calculate the exact stiffness of each pixel by using their respective correlations.</li>
	<li>The supplementary information in Figure 4 includes data such as sequence description, scan position, time, patient information, and image parameters. Use these data, particularly the image parameters, to establish the spatial relationship between MRE and IDEAL sequences.</li>
</ol>
4. 3D-Volume of liver stiffness distribution
NOTE: Each voxel in the 3D liver stiffness volume represents the stiffness value of a corresponding voxel in the 3D liver region, which is derived from the stiffness value of each pixel in Figure 4. By aligning the 3D liver region in Figure 3 with the stiffness map in Figure 4, the stiffness value of each voxel can be extracted, resulting in the generation of the 3D liver stiffness volume.
<ol>
	<li>Invoke the LSD_Slice function with the 3D liver region shown in Figure 3 and the Liver stiffness map in Figure 4 as input parameters to obtain the 3D-Volume of liver stiffness distribution, as shown in Figure 5.</li>
	<li>View the stiffness map of each layer of the liver by dragging the scroll bar below the GUI shown in Figure 5. 
	NOTE: However, unlike Figure 4, only liver tissue is accurately retained here.</li>
	<li>Observe the icons in the upper right corner of the GUI (Figure 5) such as zooming in, zooming out, returning to the global view, and marking the coordinates of the pixel selected. 
	NOTE: The default color bar is the colormap of &#34;jet&#34; which means that the corresponding values (Unit kpa) from blue to red are low to high.</li>
	<li>Execute the LSD_Volume function with the same input as LSD_Slice to obtain the spatial distribution of the 3D liver LSD, as shown in Figure 6. View the 3D-volume of LSD from any perspective by holding down the left mouse button and dragging the screen (Figure 6).</li>
</ol>
5. LSD quantitative analysis
NOTE: An important quantitative analysis focus of this study is to provide the proportion of different stages of LSD voxels in the patient&#39;s liver. Figure 6 shows that the distribution of liver fibrosis in patients is uneven in different spatial locations. The reason why clinical symptoms are not yet obvious is mainly due to a considerable proportion of liver tissue being in a normal stage. Therefore, it is necessary to quantify precisely the difference between patients and healthy individuals. This is an important quantitative concept of this study.
<ol>
	<li>Determine the numerical ranges of stiffness values for different stages of hepatic fibrosis, as shown in Figure 7.</li>
	<li>Calculate the distribution of the patient&#39;s entire liver voxels in different fibrosis stages (Figure 8) by invoking the Hepatic_Fibrosis function with the input parameter of the 3D-volume of LSD shown in Figure 6.</li>
	<li>Use the same steps to calculate and compare the results of a completely healthy liver with the typical liver fibrosis patient described above (Figure 9).</li>
</ol>

Quantitative Analysis of Liver Stiffness Distribution

<ol><li>Henderson, N. C., Rieder, F., Wynn, T. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fibrosis%3A+from+mechanisms+to+medicines%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Fibrosis: from mechanisms to medicines.</a> Nature. 587 (7835), 555-566 (2020).</li>
<li>Parola, M., Pinzani, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Liver+fibrosis%3A+Pathophysiology%2C+pathogenetic+targets+and+clinical+issues%5BTitle%5D)+AND+%22Molecular+Aspects+of+Medicine%22%5BJournal%5D)">Liver fibrosis: Pathophysiology, pathogenetic targets and clinical issues.</a> Molecular Aspects of Medicine. 65, 37-55 (2019).</li>
<li>Ramachandran, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Resolving+the+fibrotic+niche+of+human+liver+cirrhosis+at+single-cell+level%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Resolving the fibrotic niche of human liver cirrhosis at single-cell level.</a> Nature. 575 (7783), 512-518 (2019).</li>
<li>Stefan, N., Häring, H. -U., Cusi, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Non-alcoholic+fatty+liver+disease%3A+causes%2C+diagnosis%2C+cardiometabolic+consequences%2C+and+treatment+strategies%5BTitle%5D)+AND+%22The+Lancet.+Diabetes+%26+Endocrinology%22%5BJournal%5D)">Non-alcoholic fatty liver disease: causes, diagnosis, cardiometabolic consequences, and treatment strategies.</a> The Lancet. Diabetes & Endocrinology. 7 (4), 313-324 (2019).</li>
<li>Castera, L., Friedrich-Rust, M., Loomba, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Noninvasive+assessment+of+liver+disease+in+patients+with+nonalcoholic+fatty+liver+disease%5BTitle%5D)+AND+%22Gastroenterology%22%5BJournal%5D)">Noninvasive assessment of liver disease in patients with nonalcoholic fatty liver disease.</a> Gastroenterology. 156 (5), 1264.e4-1281.e4 (2019).</li>
<li>Godoy-Matos, A. F., Silva Júnior, W. S., Valerio, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((NAFLD+as+a+continuum%3A+from+obesity+to+metabolic+syndrome+and+diabetes%5BTitle%5D)+AND+%22Diabetology+%26+Metabolic+Syndrome%22%5BJournal%5D)">NAFLD as a continuum: from obesity to metabolic syndrome and diabetes.</a> Diabetology & Metabolic Syndrome. 12 (1), 1-20 (2020).</li>
<li>Venkatesh, S. K., Xu, S., Tai, D., Yu, H., Wee, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Correlation+of+MR+elastography+with+morphometric+quantification+of+liver+fibrosis+%28Fibro-C-Index%29+in+chronic+hepatitis+B%5BTitle%5D)+AND+%22Magnetic+Resonance+in+Medicine%22%5BJournal%5D)">Correlation of MR elastography with morphometric quantification of liver fibrosis (Fibro-C-Index) in chronic hepatitis B.</a> Magnetic Resonance in Medicine. 72 (4), 1123-1129 (2014).</li>
<li>Yin, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Assessment+of+hepatic+fibrosis+with+magnetic+resonance+elastography%5BTitle%5D)+AND+%22Clinical+Gastroenterology+and+Hepatology%22%5BJournal%5D)">Assessment of hepatic fibrosis with magnetic resonance elastography.</a> Clinical Gastroenterology and Hepatology. 5 (10), 1207-1213 (2007).</li>
<li>Venkatesh, S. K., Wang, G., Lim, S. G., Wee, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Magnetic+resonance+elastography+for+the+detection+and+staging+of+liver+fibrosis+in+chronic+hepatitis+B%5BTitle%5D)+AND+%22European+Radiology%22%5BJournal%5D)">Magnetic resonance elastography for the detection and staging of liver fibrosis in chronic hepatitis B.</a> European Radiology. 24, 70-78 (2014).</li>
<li>Ichikawa, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Magnetic+resonance+elastography+for+staging+liver+fibrosis+in+chronic+hepatitis+C%5BTitle%5D)+AND+%22Magnetic+Resonance+in+Medical+Sciences%22%5BJournal%5D)">Magnetic resonance elastography for staging liver fibrosis in chronic hepatitis C.</a> Magnetic Resonance in Medical Sciences. 11 (4), 291-297 (2012).</li>
<li>Chen, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Early+detection+of+nonalcoholic+steatohepatitis+in+patients+with+nonalcoholic+fatty+liver+disease+by+using+MR+elastography%5BTitle%5D)+AND+%22Radiology%22%5BJournal%5D)">Early detection of nonalcoholic steatohepatitis in patients with nonalcoholic fatty liver disease by using MR elastography.</a> Radiology. 259 (3), 749-756 (2011).</li>
<li>Singh, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Diagnostic+performance+of+magnetic+resonance+elastography+in+staging+liver+fibrosis%3A+a+systematic+review+and+meta-analysis+of+individual+participant+data%5BTitle%5D)+AND+%22Clinical+Gastroenterology+and+Hepatology%22%5BJournal%5D)">Diagnostic performance of magnetic resonance elastography in staging liver fibrosis: a systematic review and meta-analysis of individual participant data.</a> Clinical Gastroenterology and Hepatology. 13 (3), 440.e6-451.e6 (2015).</li>
<li>Ferro, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Radiomics+in+prostate+cancer%3A+an+up-to-date+review%5BTitle%5D)+AND+%22Therapeutic+Advances+in+Urology%22%5BJournal%5D)">Radiomics in prostate cancer: an up-to-date review.</a> Therapeutic Advances in Urology. 14, 17562872221109020(2022).</li>
<li>Nam, D., Chapiro, J., Paradis, V., Seraphin, T. P., Kather, J. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Artificial+intelligence+in+liver+diseases%3A+Improving+diagnostics%2C+prognostics+and+response+prediction%5BTitle%5D)+AND+%22JHEP+Reports%22%5BJournal%5D)">Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction.</a> JHEP Reports. 4 (4), 100443(2022).</li>
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</ol>

The software tool for hepatic fibrosis quantification listed in the Table of Materials of this study, HepaticFibrosis V1.0, is a software tool from Beijing Intelligent Entropy Science &amp; Technology Co Ltd. The intellectual property rights of this software tool belong to the company.

In clinical practice, it is challenging to accurately quantify and track the condition of early-stage hepatic fibrosis patients. The comparison shown in Figure 9 fully reflects the degree of hepatic fibrosis in the patient compared to a healthy liver; of course, this figure can also be a comparison between two different periods for the patient, used for evaluation of treatment efficacy. This precise quantification method is the core critical step of this study. Furthermore, the calculation m...

Hepatic fibrosis refers to the formation of excessive scar tissue in the liver, usually as a result of liver damage or disease1,2,3,4. It frequently arises as a consequence of chronic liver injury and is commonly associated with liver diseases, such as chronic viral hepatitis, non-alcoholic fatty liver disease, and alcoholic liver disease. If left untreated, hepatic fibrosis can progress to cirrhosis, a potentially life-threatening condition associated with significant morbidity and mortality.
Active research in this area aims to elucidate the cellular and molecular mechanisms underlying the pathogenesis of hepatic fibrosis, as well as to develop novel diagnostic and therapeutic strategies to improve patient outcomes. Another objective is the noninvasive detection of the hepatic fibrosis stage, which is a critical aspect that directly correlates with disease diagnosis, treatment selection, and prognosis evaluation. Despite the importance of accurate diagnosis and the monitoring of hepatic fibrosis, traditional diagnostic methods, such as liver biopsy, are invasive and associated with significant risks. In contrast, magnetic resonance elastography5,6 (MRE) is a promising non-invasive imaging technique that has demonstrated potential in the diagnosis and monitoring of hepatic fibrosis by quantifying liver stiffness.
In recent years, there has been significant research focused on evaluating the accuracy and reliability of MRE in the diagnosis of hepatic fibrosis, as well as its potential advantages over traditional diagnostic methods. The liver stiffness metric of MRE has been granted approval by the United States Food and Drug Administration (FDA) for clinical diagnosis, and extensive comparative analysis with pathological results has been conducted in clinical practice. The results have shown that the stiffness maps generated by MRE exhibit a strong positive correlation with various stages of liver fibrosis7,8,9,10,11,12. Yet so far, the work of accurately evaluating and tracking the progression of liver fibrosis in patients through quantitative analysis of liver stiffness distribution (LSD) by matching liver structure images with MRE has not made much progress.
In this study, the medical imaging group analysis technique13,14,15 is introduced to achieve accurate alignment of the liver structure images with the stiffness map generated by MRE in 3D space, enabling the calculation of liver stiffness values for each voxel of the entire liver. Based on the 3D-digital model of LSD, the exact distribution of patient-specific liver fibrosis staging can be calculated and evaluated. This lays a solid foundation for the precise quantitative diagnosis of early-stage liver fibrosis.

<table><tbody><tr><td>MATLAB</td><td>MathWorks&amp;nbsp;</td><td>2022B</td><td>Computing and visualization&amp;nbsp;</td></tr><tr><td>Mimics</td><td>Materialise</td><td>Mimics Research V20</td><td>Model format transformation</td></tr><tr><td>Tools for 3D_LSD</td><td>Intelligent&amp;nbsp;Entropy</td><td>HepaticFibrosis V1.0</td><td>Beijing Intelligent Entropy Science &amp;amp; Technology Co Ltd.&lt;br/&gt; Modeling for CT/MRI fusion</td></tr></tbody></table>

a three-dimensional digital model for early diagnosis of hepatic fibrosis based on magnetic resonance elastography

Hepatic fibrosis is an early stage of liver cirrhosis, and there are no better non-invasive and convenient methods for the detection and evaluation of the disease. Despite the good progress made with the liver stiffness map (LSM) based on magnetic resonance elastography (MRE), there are still some limitations that need to be overcome, including manual focus determination, manual selection of regions of interest (ROIs), and discontinuous LSM data without structural information, which makes it impossible to evaluate the liver as a whole. In this study, we propose a novel three-dimensional (3D) digital model for the early diagnosis of hepatic fibrosis based on MRE.
MRE is a non-invasive imaging technique that employs magnetic resonance imaging (MRI) to measure the liver stiffness at the scanning site through human-computer interaction. Studies have indicated a significant positive correlation between the LSM obtained through MRE and the degree of hepatic fibrosis. However, for clinical purposes, a comprehensive and precise quantification of the degree of hepatic fibrosis is necessary. To address this, the concept of Liver Stiffness Distribution (LSD) was proposed in this study, which refers to the 3D stiffness volume of each liver voxel obtained by the alignment of 3D liver tissue images and MRE indicators. This provides a more effective clinical tool for the diagnosis and treatment of hepatic fibrosis.

By utilizing the information in the Description_Name field of DICOM files, the original MRI folder can be renamed to facilitate the rapid localization of the required imaging sequence during the analysis process in the imaging group.&#160;The MRI-IDEAL out-phase sequence is the type of MRI sequence used for providing clearer descriptions of liver tissue boundaries. This is because the MRI-IDEAL out-of-phase sequence can better differentiate the magnetization strength and angle of different tissues throug...

Watch this Scientific Journal Video about Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI at JoVE.com

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI 

The objective of this study was to develop a novel three-dimensional digital model for the early diagnosis of hepatic fibrosis, which includes the stiffness of each voxel in the patient's liver and can thus, be used to calculate the distribution ratio of the patient's liver at different fibrosis stages.

A Three-Dimensional Digital Model for Early Diagnosis of Hepatic Fibrosis Based on Magnetic Resonance Elastography

Hepatic fibrosis is an early stage of liver cirrhosis, and there are no better non-invasive and convenient methods for the detection and evaluation of the ...

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1.1K Views.  Capital Medical University. The objective of this study was to develop a novel three-dimensional digital model for the early diagnosis of hepatic fibrosis, which includes the stiffness of each voxel in the patient's liver and can thus, be used to calculate the distribution ratio of the patient's liver at different fibrosis stages.

Video: Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI 

Magnetic Resonance Elastography Methodology for the Evaluation of Tissue Engineered Construct Growth

Traditional mechanical testing often results in the destruction of the sample, and in the case of long term tissue engineered construct studies, the use of destructive assessment is not acceptable. A proposed alternative is the use of an imaging process called magnetic resonance elastography. Elastography is a nondestructive method for determining the engineered outcome by measuring local mechanical property values (i.e., complex shear modulus), which are essential markers for identifying the structure and functionality of a tissue. As a noninvasive means for evaluation, the monitoring of engineered constructs with imaging modalities such as magnetic resonance imaging (MRI) has seen increasing interest in the past decade1. For example, the magnetic resonance (MR) techniques of diffusion and relaxometry have been able to characterize the changes in chemical and physical properties during engineered tissue development2. The method proposed in the following protocol uses microscopic magnetic resonance elastography (&mu;MRE) as a noninvasive MR based technique for measuring the mechanical properties of small soft tissues3. MRE is achieved by coupling a sonic mechanical actuator with the tissue of interest and recording the shear wave propagation with an MR scanner4. Recently, &mu;MRE has been applied in tissue engineering to acquire essential growth information that is traditionally measured using destructive mechanical macroscopic techniques5. In the following procedure, elastography is achieved through the imaging of engineered constructs with a modified Hahn spin-echo sequence coupled with a mechanical actuator. As shown in Figure 1, the modified sequence synchronizes image acquisition with the transmission of external shear waves; subsequently, the motion is sensitized through the use of oscillating bipolar pairs. Following collection of images with positive and negative motion sensitization, complex division of the data produce a shear wave image. Then, the image is assessed using an inversion algorithm to generate a shear stiffness map6. The resulting measurements at each voxel have been shown to strongly correlate (R2&gt;0.9914) with data collected using dynamic mechanical analysis7. In this study, elastography is integrated into the tissue development process for monitoring human mesenchymal stem cell (hMSC) differentiation into adipogenic and osteogenic constructs as shown in Figure 2.

The procedure demonstrates the methodology of magnetic resonance elastography for monitoring the engineered outcome of adipose and osteogenic tissue engineered constructs through noninvasive local assessment of the mechanical properties using microscopic magnetic resonance elastography (&mu;MRE).

Traditional mechanical testing often results in the destruction of the sample, and in the case of long term tissue engineered construct studies, the use ...

Bioengineering

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional (3D) Model

It is now well known that the cellular and tissue microenvironment are critical regulators influencing tumor initiation and progression. Moreover, the extracellular matrix (ECM) has been demonstrated to be a critical regulator of cell behavior in culture and homeostasis in vivo. The current approach of culturing cells on two-dimensional (2D), plastic surfaces results in the disturbance and loss of complex interactions between cells and their microenvironment. Through the use of three-dimensional (3D) culture assays, the conditions for cell-microenvironment interaction are established resembling the in vivo microenvironment. This article provides a detailed methodology to grow breast cancer cells in a 3D basement membrane protein matrix, exemplifying the potential of 3D culture in the assessment of cell invasion into the surrounding environment. In addition, we discuss how these 3D assays have the potential to examine the loss of signaling molecules that regulate epithelial morphology by immunostaining procedures. These studies aid to identify important mechanistic details into the processes regulating invasion, required for the spread of breast cancer.

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional 3D Model

This article provides detailed methodologies for the use of three-dimensional (3D) assays to quantify breast cancer cell invasion. Specifically, we discuss the procedures required to set up such assays, quantification, and data analysis, as well as methods to examine the loss of membrane integrity that occurs when cells invade.

It is now well known that the cellular and tissue microenvironment are critical regulators influencing tumor initiation and progression. Moreover, the ...

A Preclinical Murine Model of Hepatic Metastases

Numerous murine models have been developed to study human cancers and advance the understanding of cancer treatment and development. Here, a preclinical, murine pancreatic tumor model of hepatic metastases via a hemispleen injection of syngeneic murine pancreatic tumor cells is described. This model mimics many of the clinical conditions in patients with metastatic disease to the liver. Mice consistently develop metastases in the liver allowing for investigation of the metastatic process, experimental therapy testing, and tumor immunology research.

A preclinical, murine model of hepatic metastases performed via a hemispleen injection technique.

Numerous murine models have been developed to study human cancers and advance the understanding of cancer treatment and development. Here, a preclinical, ...

Three-dimensional Co-culture Model for Tumor-stromal Interaction

Cancer progression (initiation, growth, invasion and metastasis) occurs through interactions between malignant cells and the surrounding tumor stromal cells. The tumor microenvironment is comprised of a variety of cell types, such as fibroblasts, immune cells, vascular endothelial cells, pericytes and bone-marrow-derived cells, embedded in the extracellular matrix (ECM). Cancer-associated fibroblasts (CAFs) have a pro-tumorigenic role through the secretion of soluble factors, angiogenesis and ECM remodeling. The experimental models for cancer cell survival, proliferation, migration, and invasion have mostly relied on two-dimensional monocellular and monolayer tissue cultures or Boyden chamber assays. However, these experiments do not precisely reflect the physiological or pathological conditions in a diseased organ. To gain a better understanding of tumor stromal or tumor matrix interactions, multicellular and three-dimensional cultures provide more powerful tools for investigating intercellular communication and ECM-dependent modulation of cancer cell behavior. As a platform for this type of study, we present an experimental model in which cancer cells are cultured on collagen gels embedded with primary cultures of CAFs.

Here we present a protocol to co-culture in three-dimensions, which is useful for investigating multicellular interactions and extracellular matrix-dependent modulation of cancer cell behavior. In this experimental model, cancer cells are cultured on collagen gels embedded with human cancer-associated fibroblasts.

Cancer progression (initiation, growth, invasion and metastasis) occurs through interactions between malignant cells and the surrounding tumor stromal ...

Assessing Hepatic Steatosis with 3D Fluorescence Lipid Droplet Reconstruction

Analysis of Fluorescent-Stained Lipid Droplets with 3D Reconstruction for Hepatic Steatosis Assessment

Lipid droplets (LDs) are specialized organelles that mediate lipid storage and play a very important role in suppressing lipotoxicity and preventing dysfunction caused by free fatty acids (FAs). The liver, given its critical role in the body's fat metabolism, is persistently threatened by the intracellular accumulation of LDs in the form of both microvesicular and macrovesicular hepatic steatosis. The histologic characterization of LDs is typically based on lipid-soluble diazo dyes, such as Oil Red O (ORO) staining, but a number of disadvantages consistently hamper the use of this analysis with liver specimens. More recently, lipophilic fluorophores 493/503 have become popular for visualizing and locating LDs due to their rapid uptake and accumulation into the neutral lipid droplet core. Even though most applications are well-described in cell cultures, there is less evidence demonstrating the reliable use of lipophilic fluorophore probes as an LD imaging tool in tissue samples. Herein, we propose an optimized boron dipyrromethene (BODIPY) 493/503-based protocol for the evaluation of LDs in liver specimens from an animal model of high-fat diet (HFD)-induced hepatic steatosis. This protocol covers liver sample preparation, tissue sectioning, BODIPY 493/503 staining, image acquisition, and data analysis. We demonstrate an increased number, intensity, area ratio, and diameter of hepatic LDs upon HFD feeding. Using orthogonal projections and 3D reconstructions, it was possible to observe the full content of neutral lipids in the LD core, which appeared as nearly spherical droplets. Moreover, with the fluorophore BODIPY 493/503, we were able to distinguish microvesicles (1 &#181;m &lt; d &#8804; 3 &#181;m), intermediate vesicles (3 &#181;m &lt; d &#8804; 9 &#181;m), and macrovesicles (d &gt; 9 &#181;m), allowing the successful discrimination of microvesicular and macrovesicular steatosis. Overall, this BODIPY 493/503 fluorescence-based protocol is a reliable and simple tool for hepatic LD characterization and may represent a complementary approach to the classical histological protocols.

Author Spotlight: Analysis of Fluorescent-Stained Lipid Droplets with 3D Reconstruction for Hepatic Steatosis Assessment  

Herein, we demonstrate an optimized BODIPY 493/503 fluorescence-based protocol for lipid droplet characterization in liver tissue. Through the use of orthogonal projections and 3D reconstructions, the fluorophore allows for successful discrimination between microvesicular and macrovesicular steatosis and may represent a complementary approach to the classical histological protocols for hepatic steatosis assessment.

Lipid droplets (LDs) are specialized organelles that mediate lipid storage and play a very important role in suppressing lipotoxicity and preventing ...

Biochemistry

Three-Dimensional (3D) Tumor Spheroid Invasion Assay

Invasion of surrounding normal tissues is generally considered to be a key hallmark of malignant (as opposed to benign) tumors. For some cancers in particular (e.g., brain tumors such as glioblastoma multiforme and squamous cell carcinoma of the head and neck &#8211; SCCHN) it is a cause of severe morbidity and can be life-threatening even in the absence of distant metastases. In addition, cancers which have relapsed following treatment unfortunately often present with a more aggressive phenotype. Therefore, there is an opportunity to target the process of invasion to provide novel therapies that could be complementary to standard anti-proliferative agents. Until now, this strategy has been hampered by the lack of robust, reproducible assays suitable for a detailed analysis of invasion and for drug screening. Here we provide a simple micro-plate method (based on uniform, self-assembling 3D tumor spheroids) which has great potential for such studies. We exemplify the assay platform using a human glioblastoma cell line and also an SCCHN model where the development of resistance against targeted epidermal growth factor receptor (EGFR) inhibitors is associated with enhanced matrix-invasive potential. We also provide two alternative methods of semi-automated quantification: one using an imaging cytometer and a second which simply requires standard microscopy and image capture with digital image analysis.

Three-Dimensional 3D Tumor Spheroid Invasion Assay

Invasion of surrounding normal tissues is a defining characteristic of malignant tumors. We provide here a simple, semi-automated micro-plate assay of invasion into a natural 3D biomatrix that has been exemplified with a number of models of advanced human cancers.

Invasion of surrounding normal tissues is generally considered to be a key hallmark of malignant (as opposed to benign) tumors. For some cancers in ...

3D Imaging of the Liver Extracellular Matrix in a Mouse Model of Non-Alcoholic Steatohepatitis

Non-alcoholic steatohepatitis (NASH) is the most common chronic liver disease in the United States, affecting more than 70 million Americans. NASH can progress to fibrosis and eventually to cirrhosis, a significant risk factor for hepatocellular carcinoma. The extracellular matrix (ECM) provides structural support and maintains liver homeostasis via matricellular signals. Liver fibrosis results from an imbalance in the dynamic ECM remodeling process and is characterized by excessive accumulation of structural elements and associated changes in glycosaminoglycans. The typical fibrosis pattern of NASH is called &#34;chicken wire,&#34; which usually consists of zone 3 perisinusoidal/pericellular fibrosis, based on features observed by Masson&#39;s trichrome stain and Picrosirius Red stains. However, these traditional thin two-dimensional (2D) tissue slide-based imaging techniques cannot demonstrate the detailed three-dimensional (3D) ECM structural changes, limiting the understanding of the dynamic ECM remodeling in liver fibrosis.
The current work optimized a fast and efficient protocol to image the native ECM structure in the liver via decellularization to address the above challenges. Mice were fed either with chow or fast-food diet for 14 weeks. Decellularization was performed after in situ portal vein perfusion, and the two-photon microscopy techniques were applied to image and analyze changes in the native ECM. The 3D images of the normal and NASH livers were reconstituted and analyzed. Performing in situ perfusion decellularization and analyzing the scaffold by two-photon microscopy provided a practical and reliable platform to visualize the dynamic ECM remodeling in the liver.

The present protocol optimizes the liver in situ perfusion/decellularization and two-photon microscopy methods to establish a reliable platform to visualize the dynamics of extracellular matrix (ECM) remodeling during non-alcoholic steatohepatitis (NASH).

Non-alcoholic steatohepatitis (NASH) is the most common chronic liver disease in the United States, affecting more than 70 million Americans. NASH can ...

A Comprehensive 3D-Liver Fat Function Distribution Model Using Dixon MRI

A 3D Quantification Technique for Liver Fat Fraction Distribution Analysis Using Dixon Magnetic Resonance Imaging

This study presents a 3D quantification methodology for the distribution of liver fat fraction (LFF) through the utilization of Dixon MRI image analysis. The central aim is to offer a highly accurate and non-invasive means of evaluating liver fat content. The process involves the acquisition of In-phase and Water-phase images from a Dixon sequence. LFF maps are then meticulously computed voxel by voxel by dividing the Lipid Phase images by the In-phase images. Simultaneously, 3D liver contours are extracted from the In-phase images. These crucial components are seamlessly integrated to construct a comprehensive 3D-LFF distribution model. This technique is not limited to healthy livers but extends to those afflicted by hepatic steatosis. The results obtained demonstrate the remarkable effectiveness of this approach in both visualizing and quantifying liver fat content. It distinctly discerns patterns that differentiate between normal and steatotic livers. By harnessing Dixon MRI to extract the 3D structure of the liver, this method offers precise LFF assessments spanning the entirety of the organ, thereby holding great promise for the diagnosis of hepatic steatosis with remarkable effectiveness.

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI 

This study introduces a unique 3D quantification method for liver fat fraction (LFF) distribution using Dixon Magnetic Resonance Imaging (Dixon MRI). LFF maps, derived from in-phase and water-phase images, are integrated with 3D liver contours to differentiate LFF patterns between normal and steatotic livers, enabling precise assessment of liver fat content.

This study presents a 3D quantification methodology for the distribution of liver fat fraction (LFF) through the utilization of Dixon MRI image analysis. ...

Application of Ultrasound and Shear Wave Elastography Imaging in a Rat Model of NAFLD/NASH

Nonalcoholic Steatohepatitis (NASH) is a condition within the spectrum of Non-Alcoholic Fatty Liver Disease (NAFLD), which is characterized by liver fat accumulation (steatosis) and inflammation leading to fibrosis. Preclinical models closely recapitulating human NASH/NAFLD are essential in drug development. While liver biopsy is currently the gold standard for measuring NAFLD/NASH progression and diagnosis in the clinic, in the preclinical space, either collection of whole liver samples at multiple timepoints during a study or biopsy of liver is needed for histological analysis to assess the disease stage.
Conducting a liver biopsy mid-study is an invasive and labor-intensive procedure, and collecting liver samples to assess disease level increases the number of research animals needed for a study. Thus, there is a need for a reliable, translatable, non-invasive imaging biomarker to detect NASH/NAFLD in these preclinical models. Non-invasive ultrasound-based B-mode images and Shear Wave Elastography (SWE) can be used to measure steatosis as well as liver fibrosis. To assess the utility of SWE in preclinical rodent models of NASH, animals were placed on a pro-NASH diet&#160;and underwent non-invasive ultrasound B-mode and shear wave elastography imaging to measure hepatorenal (HR) index and liver elasticity, measuring progression of both liver fat accumulation and tissue stiffness, respectively, at multiple time points over the course of a given NAFLD/NASH study.
The HR index and elasticity numbers were compared to histological markers of steatosis and fibrosis. The results showed strong correlation between the HR index and percentage of Oil Red O (ORO) staining, as well as between elasticity and Picro-Sirius Red (PSR) staining of livers. The strong correlation between classic ex vivo methods and in vivo imaging results provides evidence that shear wave elastography/ultrasound-based imaging can be used to assess disease phenotype and progression in a preclinical model of NAFLD/NASH.

This protocol describes the use of an enhanced ultrasound technique to non-invasively observe and quantify liver tissue changes in rodent models of nonalcoholic fatty liver disease.

Nonalcoholic Steatohepatitis (NASH) is a condition within the spectrum of Non-Alcoholic Fatty Liver Disease (NAFLD), which is characterized by liver fat ...

Biology

Measurement of Liver Stiffness Using Atomic Force Microscopy Coupled with Polarization Microscopy

Matrix stiffening has been recognized as one of the key drivers of the progression of liver fibrosis. It has profound effects on various aspects of cell behavior such as cell function, differentiation, and motility. However, as these processes are not homogeneous throughout the whole organ, it has become increasingly important to understand changes in the mechanical properties of tissues on the cellular level.
To be able to monitor the stiffening of collagen-rich areas within the liver lobes, this paper presents a protocol for measuring liver tissue elastic moduli with high spatial precision by atomic force microscopy (AFM). AFM is a sensitive method with the potential to characterize local mechanical properties, calculated as Young&#39;s (also referred to as elastic) modulus. AFM coupled with polarization microscopy can be used to specifically locate the areas of fibrosis development based on the birefringence of collagen fibers in tissues. Using the presented protocol, we characterized the stiffness of collagen-rich areas from fibrotic mouse livers and corresponding areas in the livers of control mice.
A prominent increase in the stiffness of collagen-positive areas was observed with fibrosis development. The presented protocol allows for a highly reproducible method of AFM measurement, due to the use of mildly fixed liver tissue, that can be used to further the understanding of disease-initiated changes in local tissue mechanical properties and their effect on the fate of neighboring cells.

We present a protocol to measure the elastic moduli of collagen-rich areas in normal and diseased liver using atomic force microscopy. The simultaneous use of polarization microscopy provides high spatial precision for localizing collagen-rich areas in the liver sections.

Matrix stiffening has been recognized as one of the key drivers of the progression of liver fibrosis. It has profound effects on various aspects of cell ...