The Seminar Series invites spatial tissue profiling researchers, technology developers, and stakeholders to share breaking new discoveries, innovations.
Location: It is 100% online and takes place on the last Friday of each month. If there are breaking news or technologies to be showcased, additional seminars can be arranged outside the standard monthly slot.
Time: Last Friday of the month, 7AM Pacific time, 10AM Eastern, 3PM London, 4PM CET, 6PM Athens, 7:30PM Delhi, 10PM Beijing/Singapore/Perth, 11:00PM Tokyo, 11:30PM Adelaide, 12AM Melbourne/Sydney
How Can I Attend? All GESTALT members receive an automated invite with the seminar link. Joining GESTALT is free! Please use this link to become a member: https://globalspatial.org/join-gestalt/
Dr. Mengwei (Carol) Hu was born and raised in Bengbu, Anhui, China. She earned her B.S. in Biological Sciences at Peking University, and subsequently pursued a Ph.D. in Genetics at Yale University under the supervision of Dr Siyuan (Steven) Wang. Her thesis project focused on developing an image-based pooled CRISPR screen platform of 3D genome regulators termed Perturb-Tracing (Nature Methods 2025). After graduation, she joined Merck as a senior scientist in the Department of Data, AI and Genome Sciences, and continued her research at the intersection of spatial biology and high-content CRISPR screen.
Dr. Hu will introduce her latest work at Merck, termed SPACE (SPAtial Cell Exploration), which enables spatial CRISPR screening in FFPE-embedded 3D spheroid models with whole-transcriptome-scale RNA readout and multiplexed protein detection.
Niklas Müller-Bötticher studied Molecular Biotechnology at Heidelberg University before continuing with his Masters at ETH Zurich. After a short stint at Roche, he joined the group of Naveed Ishaque at the Berlin Institute of Health, working at the interface of omics, human disease, and data science. He focuses on developing computational methods to better understand and model spatial transcriptomics data and played core roles in the development of the cell segmentation-free tool Sainsc and the spatial clustering algorithm SpatialLeiden.
Imaging-based spatially resolved transcriptomics can localize transcripts within tissue sections in 3D. Cell segmentation assigns transcripts to cells and precedes annotation of cell function. However, cell segmentation is usually performed in 2D, thus unable to deal with spatial doublets arising from overlapping cells, resulting in segmented cells containing transcripts originating from multiple cell types. Here we present a computational tool called ovrlpy that identifies overlapping cells, tissue folds, and inaccurate cell-segmentation by analyzing transcript localization in 3D.
Dr. Siyuan (Steven) Wang, is an Associate Professor of Genetics and Cell Biology at Yale School of Medicine. Research in his lab focuses on the development and application of state-of-the-art image-based omics approaches to understand the spatial organization of mammalian genome and transcriptome, and how they impact cellular states. Originally from China, Dr. Wang received a Bachelor of Science degree in Physics from Peking University in 2007. He then moved to the US and received a Ph. D. in Molecular Biology from Princeton in 2011 and later his postdoctoral training at Harvard. In 2017 he was appointed Assistant Professor by Yale University, and was promoted to the rank of Associate Professor in 2023. Dr. Wang developed/co-developed multiple influential technologies in the spatial omics field including the first-in-kind image-based 3D genomics method termed “chromatin tracing” (Science 2016) to trace the spatial folding of genome, “MERFISH” for spatial transcriptome profiling (Science 2015, Nature Methods Method of the Year 2020), and “MINA” for integrative 3D genome, spatial transcriptome and protein imaging in the same, single cells in mammalian tissue. His lab recently generated the first single-cell 3D genome atlases in cancer (Nature Genetics 2025), developed a new high-content screening technology termed “Perturb-tracing” for building the 3D genomic “regulatome” (Nature Methods 2025), and invented a new spatial transcriptomics technology termed “RAEFISH” for sequencing-free whole-genome spatial transcriptomic mapping with single-molecule resolution (Cell 2025). He received the 2011 American Physical Society Award for Outstanding Doctoral Thesis Research in Biological Physics, the 2012 Jane Coffin Childs Fellowship, the 2016 International Union of Pure and Applied Physics Young Scientist Prize in Biological Physics, the 2018 35 Innovators Under 35 of China by MIT Technology Review, the 2019 NIH Director’s New Innovator Award, the 2022 Pershing Square Sohn Prize for Young Investigators in Cancer Research, the 2023 Biophysical Society Early Career Award in Physical Cell Biology, the 2023 Hevolution/AFAR New Investigator Awards in Aging Biology and Geroscience Research, and the 2024 American Society for Cell Biology Innovation in Research Award.
Dr. Pengzhi Zhang is a Research Associate in Dr. Guangyu Wang’s lab at the Houston Methodist Research Institute, Weill Cornell Medicine. His research focuses on developing computational tools that bridge high-resolution imaging with next-generation sequencing to enable cell-level biological insights. He earned his Ph.D. in Computational Biophysics under the supervision of Prof. Margaret S. Cheung at the University of Houston, and his B.S. in Physics from the University of Science and Technology of China. Dr. Zhang develops machine learning frameworks to study molecular and imaging data, creating versatile platforms for holistic tissue analysis. His first-author work has been published in Nature Methods, Nature Biotechnology, and Nature Communications.
Dr. Zhang will introduce Thor, a computational platform that unites these two dimensions at the single-cell level. Thor integrates spot-level gene expression with tissue morphology by inferring single-cell level molecular maps.
Dr. Hanchen Wang is a postdoc with Jure Leskovec at Stanford AI Lab and Aviv Regev at Genentech. His first-author work is in Nature, Nature Biotechnology, Nature Machine Intelligence & NeurIPS. He co-organized events on “AI for Science” in ICML, ICLR, NeurIPS. He did a CS PhD at Cambridge in 3 years with Joan Lasenby from Trinity College; a Physics BS from Nanjing with Xinran Wang, where he was the commencement speaker. He secured university admission at 15 while at Suzhou High School. He spent time in finance, tech & biotech. He’s a national athlete in 400m, and enjoys reading history & biography. He is a molecular biologist and biomedical engineer whose work bridges high-resolution imaging with next-generation tissue analysis.
Dr. Wang will be presenting SpatialAgent, an AI agent dedicated to spatial biology. It integrates large language models with dynamic tool execution and adaptive reasoning, supporting the entire research pipeline, from experimental design to multimodal data analysis and hypothesis generation. In this talk, we’ll share the behind-the-scenes journey of building it, including the “aha” moments, lessons learned, and failures along the way.
Dr. Fei Chen is a Core Institute member at the Broad Institute of MIT and Harvard and an associate professor in the Department of Stem Cell and Regenerative Biology at Harvard University. Chen’s laboratory is building tools that bridge single-cell genomics with space and time, to enable discoveries of where cell types are localized within intact tissues, as well as when relevant transcriptional modules are active.
Chen obtained his Ph.D. in biological engineering from the Massachusetts Institute of Technology, where he worked with Ed Boyden. Chen was a Schmidt Fellow at the Broad Institute. His awards include the National Institutes of Health Director’s Early Independence Award, the Searle Scholars Award, the Burroughs Wellcome CASI Award, the Allen Distinguished Investigator Award, and a Merkin Institute Fellowship.