Luddy professor’s novel study of cell organization reveals new paths to better health: Luddy School of Informatics, Computing, and Engineering : Indiana University
Imagine a bustling neighborhood where residents gather at the coffee shop in the morning.
If you saw a disruption in this pattern — an early-morning line outside the pizza place, instead — you’d know something had changed in the pattern of neighborhood life.
In his paper “TrimNN: A New Way to Explore the Organization of Cells,” published August 19, 2025, in Nature Communications — an offshoot of the journal Nature — Wang and his co-authors propose exploring the rules of cell organization from a bottom-up perspective, by using TrimNN (triangulation cellular community motif neural network), a graph-based deep learning framework. This method focuses on how cells cluster in initial tiny patterns and works from there to study how cellular organization develops.
“These insights provide a foundation for understanding biological and disease mechanisms and offer potential biomarkers for diagnosis and therapeutic interventions,” they note.
Cell organization offers insights into health
Wang says the layout of “cellular neighborhoods” can tell us a lot about sickness and health, based on how cells team up.
“It influences how organs function, how diseases spread, and even how well treatments work,” he writes in a “Behind the Paper” explanation, published in Protocols & Methods and Cell & Molecular Biology, both research communities of the Springer Nature Group, on August 24, 2025.
“Cells in our body aren’t loners,” he explains. “Immune cells talk to each other to coordinate defenses. Cancer cells interact with their surroundings in ways that help them grow or evade the immune system.
“Understanding these spatial arrangements could reveal early signs of disease or help design more targeted therapies.”
Wang adds, “But until recently, we didn’t have a good way to capture the patterns in these microscopic communities. That’s where TrimNN comes in.”
Cell organization: top-down vs. bottom-up
Traditionally, researchers relied on a “top-down” method to study cellular organization.
“It’s a bit like describing a city by saying, “20% of the people here work in finance, 30% in hospitality,” without noting where those people meet or how they interact, Wang notes in his “Behind the Paper” article.
“This works up to a point, but it misses the fine details.”
But the deep learning framework TrimNN identifies cellular communities using AI methods, to reveal organizational patterns and traits, the research paper’s authors explain. These include computational innovations such as proteomics data, which studies proteins to better understand how cells work.
“TrimNN flips the script,” Wang says in his “Behind the Paper” piece. “Instead of starting with large groupings, it takes a bottom-up approach, looking for tiny recurring patterns of cells, called cellular community (CC) motifs.”
TrimNN innovatively uses a graph neural network to estimate the size of CC motifs. This network leans into geometric and grid-based deep learning to understand these cellular gatherings and how they develop — which can offer insights into health conditions such as tumor development.
Small cell patterns = big potential
“A CC motif is like a “mini-neighborhood” of cells—a handful of cells of specific types, arranged in a particular way,” Wang says.
“Why focus on these small patterns? Because they can be conserved—recurring again and again in healthy tissues — or they can shift in diseases, revealing clues about what’s going wrong.”
One example, Wang notes: “In colorectal carcinoma, TrimNN identified patterns that change dramatically from healthy to cancerous tissue as more cell types join the group. These shifts may reflect how the tumor rewires its microenvironment to evade immune attacks.”
Why motifs matter
Past “top-down” methods of studying cellular communities got bogged down because they considered all possible patterns. But TrimNN uses smart pattern recognition, allowing scientists to explore complex arrangements more easily.
“TrimNN can spot large, meaningful motifs in seconds — something older methods couldn’t realistically do,” Wang notes.
Their team of researchers, Wang says, tested TrimNN on both simulated datasets and real-world biological data to discover how it can help:
• Predict cancer outcomes
“In a study of colorectal cancer patients, TrimNN found specific motifs — combinations of macrophages (a type of immune cell) and smooth muscle cells — whose arrangement could predict patient survival,” Wang notes.
• Shed light on Alzheimer’s disease
“In Alzheimer’s mouse brains, TrimNN revealed two distinct kinds of motifs,” Wang writes in “Behind the Paper.” He says, “Some motifs tended to appear away from amyloid-beta plaques, while others were tightly co-located with them. … TrimNN also showed that these motifs had different communication ‘signatures’ between cells, and distinct sets of active genes, potentially revealing different roles in disease progression.”
Evolving tech in spatial biology
The paper’s authors note that as a tool for the spatial biology era, TrimNN is part of a rapidly evolving type of technology that studies how cells operate in their environments.
“Future work will include analyzing large-scale spatial omics data to connect the idea of cellular community motifs and functional tissue units,” the researchers note, as well as “assessing the results in different categories of diseases from multiple independent data sources.”
In his “Behind the Paper “article, Wang adds, “Spatial biology is one of the fastest-growing areas in biomedical research. As data sets get bigger and more complex, tools like TrimNN will be essential for turning raw maps of cells into actionable biological knowledge.”
He adds: “We would like to highlight Ph.D. student Shuang Wang, who substantially contributed to the work, and this work is collaborated with Dr. Dong Xu at University of Missouri.”
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