SITC 2026 Dinner Lecture

Integrating 3D pathology with molecular and microenvironment profiling

Jonathan T.C. Liu, PhD  ·  November 5, 2026  ·  6:00–8:00 PM

RSVP for the Dinner Lecture

Join Alpenglow Biosciences during SITC 2026 for an evening lecture exploring how 3D pathology can be integrated with molecular analysis and tumor microenvironment profiling.

The discussion will explore emerging approaches for connecting volumetric tissue imaging with molecular and microenvironmental information. Topics will include 3D-guided microdissection and targeted sectioning, the compatibility of 3D pathology workflows with downstream molecular assays, and 3D graph-based approaches for quantifying cellular interactions within the tumor microenvironment.

Together, these approaches point toward a more integrated view of tissue biology, connecting molecular information with cellular neighborhoods, spatial relationships, and tissue architecture in 3D.

Recent related work from Jonathan Liu and collaborators

3D pathology-guided microdissection Nature Methods, 2026 Combines 3D pathology with precision microdissection to recover selected regions for molecular analysis, including regions defined by complex 3D tissue architecture.
Read the paper ↗
Detection of Prostate Cancer in 3-Dimensional Pathology Datasets via Generative Immunolabeling Modern Pathology, 2026 Introduces SIGHT, a deep-learning approach that generates synthetic immunolabels to identify cancer-enriched regions within 3D prostate pathology datasets.
Read the paper ↗
Artificial Intelligence-informed Architectural Insights of 3-dimensional Glandular Networks Identify Patients With Prostate Cancer at a Higher Risk of Biochemical Recurrence Modern Pathology, 2026 Examines 3D glandular architecture in prostate cancer and identifies volumetric features associated with biochemical recurrence risk.
Read the paper ↗
Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification bioRxiv, 2026 Introduces SCALE3D, a graph-based method for analyzing cellular organization and interactions across large 3D pathology datasets.
Read the preprint ↗
Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessments Nature Biomedical Engineering, 2026 Presents TRICARE, a deep-learning framework that identifies high-risk sections within large 3D pathology datasets for focused pathologist review.
Read the paper ↗
Jonathan T.C. Liu, PhD

Speaker

Jonathan T.C. Liu, PhD

Biomedical Engineer, Professor of Pathology, and Co-founder of Alpenglow Biosciences

Dr. Jonathan Liu is a biomedical engineer and professor of pathology whose research focuses on high-resolution optical imaging, computational analysis, and non-destructive slide-free 3D pathology. His laboratory develops technologies for clinical decision support and surgical guidance, with an emphasis on imaging larger tissue volumes while preserving specimens for downstream molecular analysis.

The Liu lab works across the full 3D pathology workflow, including reversible optical clearing and fluorescence labeling, high-throughput open-top light-sheet microscopy, image processing, and AI-based analysis. The group also develops quantitative methods to study 3D tissue architecture, cellular organization, and spatial relationships, with applications that complement molecular profiling, genomics, and radiomics.

Dr. Liu received his B.S.E. from Princeton University and his M.S. and Ph.D. in mechanical engineering from Stanford University. He is a co-founder and board member of Alpenglow Biosciences, which has commercialized non-destructive 3D pathology technologies developed in his laboratory. His work has been supported by the NCI, NIBIB, NIDDK, Department of Defense, NSF, ARPA-H, and several foundations.

Visit the Jonathan Liu Lab ↗

RSVP

RSVP

This is a complimentary event for Pharmaceutical, Biotech, and Academic attendees.

DATE
November 5th, 2026

TIME

06:00 - 8:00 PM

LOCATION

TBD, Phoenix


CONTACT
Steve Pemberton: steve@alpenglowbiosciences.com