MIFSpace is an open-source framework for integrating spatial biology images from adjacent tissue sections that aims to remove constraints from clinical-grade multiplex immunofluorescence. Here, Riyue Bao and Quincy Gu discuss the MIFSpace framework that they presented at Pathology Visions 2026, and the importance of bridging digital pathology, molecular biology, and clinical medicine.
Riyue Bao is Associate Professor of Medicine at University of Pittsburgh.
Quincy Gu is Assistant Professor at University of Pittsburgh School of Medicine.
The following transcript has been edited for clarity.
Riyue Bao: Hello everyone. I am Dr. Riyue Bao, Associate Professor of Medicine at the University of Pittsburgh and UPMC Hillman Cancer Center. My research focuses on tumor immune interactions and resistance to immunotherapy. One thing we have learned through this journey is that spatial context matters. Beyond which cells are present, understanding how tumor cells, T- cells, macrophage, and streamer populations organize into complex ecosystems is also critical.
Multiplex immunofluorescence allows us to characterize distinct immune architectures with clinical significance, but individual panels capture only a limited number of markers. Therefore, we developed the MIFSpace: an automated and innovative computational framework. MIFSpace integrates imaging panels across sections, expanding the effective marker space for high-dimensional cellular phenotyping and spatial biology. At our translational immune lab, this opens new opportunities for us to investigate tumor immune spatial neighborhoods, immune exclusion, and multicellular ecosystems in prediction of immunotherapy response and resistance.
Quincy Gu: Hi everyone, I'm Dr. Quincy Gu, Assistant Professor at the University of Pittsburgh and affiliated with UPMC Hillman Cancer Center. My research focused on interpretable AI and multimodal computational frameworks that bridge digital pathology, molecular biology, and clinical medicine. One of the most exciting frontiers in computational pathology is moving beyond image recognition toward reconstructing the complex biological organization of human tissues. That's the computational vision behind MIFSpace, where it enables higher dimensional cellular phenotyping and the characterization of complex spatial architectures.
Importantly, different tissue sections are not identical. Our framework incorporates quantitative cross-panel concordance analysis to evaluate marker consistency, making integration more rigorous, interpretable, and biologically meaningful. What excites me is a potential to move from isolated tissue images toward multimodal tissue intelligence, connecting cellular identities, spatial relationships, and molecular measurements to better understand disease. We envision MIFSpace as a step towards scalable AI enabled spatial pathobiology transforming fragmented imaging data into integrated representations of tissue biology to advanced biomarker discovery and precision oncology.
Our broader vision is to build a multi-dimensional understanding of human tissue and translate those discoveries into new biomarkers, therapeutic targets, and better patient care. We invite you to follow our journey as we push the boundaries of artificial intelligence and spatial pathobiology.
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