Researchers Develop New Tool to Fight Kidney Disease

The cell changes are key indicators of the ultimately devastating damage that end stage renal disease can cause, but these specialized cells have proven difficult to detect.

To aid in detection, the researchers have come up with a cloud-based tool called the PodoSighter, which is described in a paper in the Journal of the American Society of Nephrology. The research is being highlighted on the cover of the journal’s November issue.

The project is an example of how advanced computational capabilities are allowing scientists to glean new information from complex images of anatomical structures.

“In the medical domain, understanding human systems depends on analyzing huge amounts of very different types of data,” says Pinaki Sarder, PhD, associate professor of pathology and anatomical sciences and senior author on the paper. “The question is, how do we combine all these data to try and understand fundamental human systems and disease?”

Working in the emerging area of computational nephropathology, Sarder and his colleagues are focused on developing a better understanding of the information found in images of kidney biopsy samples.

“It’s been known for decades that the quantity and density of podocytes are important both for diagnosis and prognosis of end stage kidney disease,” says Darshana Govind, PhD, first author, who did her doctoral work in Sarder’s lab. She is now a data scientist at Janssen Pharmaceuticals.

In the early stages of kidney disease, podocytes begin to change shape and, as the disease progresses, the number of them will fall. 

“A healthy person has more podocytes than a sick person,” Sarder says. “If one day we can track the loss of podocytes, then we can determine the stage of the disease.” Right now, that’s not possible, but it’s one of the goals of the UB research.

One of the biggest challenges in dealing with images of biopsied tissue is that they contain huge amounts of data. The additional challenge with podocytes is that they are found deep within the glomeruli, the sac-like bundles of capillaries that handle first-line filtration of blood in the kidneys.

“It’s very challenging to identify podocytes in an image,” Govind says, noting there are so many cells in the glomerulus that it’s hard even for trained pathologists to figure out which nuclei belong to podocytes. Different types of staining can be used to highlight the podocytes, but sometimes the staining causes other important image information to be lost.

The solution the researchers developed is to use a machine learning technique called convolutional neural networks, a learning algorithm that can distinguish specific objects in an image. It was developed based, to a certain degree, on the ways that the visual cortex in the human brain processes visual information.

The technique involves essentially “training” the computer to detect podocytes. 

“The tissue is prepared in the clinic and the AI-based method detects it for you,” Govind says. “You click a button and the podocytes are identified.”

Density information is also provided. 

“The PodoSighter not only detects podocytes, but it spits out a report on how many of these cells are identified in each glomerulus and what the density is, a key indicator for disease progression,” says Sarder, who noted that as kidney disease progresses, the glomerulus grows in size while the number of podocytes goes down.

Currently used primarily as a research tool, the PodoSighter can work on samples from both animals and humans. The goal is to eventually get this into routine use in clinics for human use, which the researchers say may be possible in just a few years.

The researchers conducted some of their work at UB’s Center for Computational Research.

This project was supported by several grants from the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health, including a Kidney Precision Medicine Project grant and a grant from the Human BioMolecular Atlas Program.

In addition to Sarder and Govind, other co-authors from the Department of Pathology and Anatomical Sciences are: 

• Brendon Robert Lutnick, doctoral student in computational cell biology, anatomy and pathology
• Imtiaz Mohammad, PhD, lead histotechnologist in the histology core lab
  
• John E. Tomaszewski, MD, SUNY Distinguished Professor and the Peter A. Nickerson, PhD, Professor and Chair
• Amber M. Worral, PhD, microscopy and histology core labs director

Rabi Yacoub, MD, assistant professor of medicine in the Division of Nephrology, is also a co-author.

Jeffrey C. Miecznikowski, PhD, associate professor of biostatistics in the School of Public Health and Health Professions, is another co-author. 

Other co-authors are from:

• Johns Hopkins University
• Kitware Inc.
• Université de Paris
• University of California, Davis
• University of California, San Francisco
• University Hospital Cologne
• University Hospital Essen
• University Medical Center Hamburg-Eppendorf