Bioengineering professor Yang Liu’s team, including first author Phuong “Hong” Nguyen, has developed a novel approach to immunofluorescence that is easier, faster, and more accessible. The discovery, called Universal Multiplexed Immunofluorescence (umIF), is outlined in a paper published inSmall Methods. "Our goal was not simply to create another multiplexed [immunofluorescence] protocol," said Liu, "but to make spatial protein profiling more practical for real clinical specimens and large-scale tissue mapping studies.”
Written by Ben Libman
Universal Multiplexed Immunofluorescence (umIF) could help doctors better predict how a patient’s cancer will behave and which treatments are most likely to succeed
When performing experiments, scientists often need to locate specific proteins in a tissue sample. A common way to do this is using immunofluorescence, where antibodies are attached to fluorescent dyes that light up target proteins under a microscope.
This method has several limitations. It can be slow, expensive, difficult to scale, and often requires antibodies raised in different animal species to avoid interference between them, limiting researchers' flexibility.
Professor Yang Liu
Bioengineering professor Yang Liu set out to create a better system for her laboratory. “We wanted to overcome this bottleneck and make highly multiplexed protein mapping more efficient, scalable, and compatible with routine pathology tissue,” said Liu. “Our goal was not simply to create another multiplexed [immunofluorescence] protocol, but to make spatial protein profiling more practical for real clinical specimens and large-scale tissue mapping studies.”
Liu’s team, including first author Phuong “Hong” Nguyen, has developed a novel approach that is easier, faster, and more accessible. The discovery, called Universal Multiplexed Immunofluorescence (umIF), is outlined in a paper published inSmall Methods.
This new technique is accomplished by “macromolecular crowding”: Liu’s team added inert, high-molecular-weight polymers and reduced the amount of free space available for diffusion. This increases the likelihood that antibodies encounter and bind to their target proteins. The resulting method leads to more reliable staining, without fundamentally altering the chemistry of immunofluorescence. In addition, umIF produces brighter images, allows researchers to image more proteins simultaneously, and works through multiple rounds of imaging.
Beyond improving laboratory workflows, this technology may also contribute to scientists’ understanding of the tumor microenvironment in cancer. When testing umIF in a lung cancer model, the team was able to identify two distinct populations of macrophages —immune cells that play a crucial role in the body’s response to cancer —based on their shape, location within the tumor, and epigenetic state. These differences suggest the cells may play distinct roles within the tumor microenvironment, a possibility the researchers hope to investigate further. If these findings are validated, umIF could help doctors better predict how a patient’s cancer will behave and which treatments are most likely to succeed.
Liu looks forward to continuing to develop and improve umIF. Next, the team plans to address one of the field's biggest remaining challenges: reducing reliance on large antibody panels to predict cellular functional states from tissue morphology and a smaller set of molecular markers. Liu hopes to use the detailed molecular maps generated by umIF to better train artificial intelligence models. Those models could further streamline the imaging process by requiring fewer antibodies and making large-scale studies faster and more affordable. Liu wants to expand access to the technology, adding “we hope that other laboratories can adapt this approach to improve their workflow in multiplexed immunofluorescence staining.”
By making advanced protein mapping more accessible, Liu’s work represents another way Bioengineering at Illinois is advancing the science of human health.