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DTSTART;TZID=Australia/Sydney:20260601T130000
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DTSTAMP:20260601T145743
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SUMMARY:Through the Unlabeled Lens of Spatial Multi-Omics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Anthony A. Fung\, Yale University \n\n\n\nThis is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nSometimes it matters less what you look at\, and more what you see. Common practices in clinical pathology often involve multiple histological stains on serial sections of tissue biopsy to obtain the highest diagnostic power\, but this requires expertise\, reagent costs\, consumes tissue\, risks deformation\, and complicates co-registration\, potentially missing rare microstructures. Now there is a major push for spatial multi-omics integration\, but even adjacent tissue sections captured with different modalities decrease performance. Today’s seminar introduces a non-destructive label-free optical platform combining SRS\, SHG\, and TPF enables high-resolution molecular imaging to unravel the lipidomic\, metabolic\, and morphometric landscape of kidney disease\, and how these data types can augment your modalities. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Anthony A. Fung\n\n\n\nDr Anthony A. Fung is a T32 postdoctoral fellow in Professor Rong Fan’s group at Yale University. He received his PhD in Bioengineering at University of California San Diego from Professor Lingyan Shi’s group. Anthony has received several awards in the quantitative spatial biology field and is a collaborating investigator in both HuBMAP and SenNet consortia. His current work centers on the development and application of spatial multi-omics technologies in aging and immune senescence.Find out more on LinkedIn:https://www.linkedin.com/in/anthony-fung/
URL:https://spds.sydney.edu.au/event/through-the-unlabeled-lens-of-spatial-multi-omics/
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DTSTART;TZID=Australia/Sydney:20260615T130000
DTEND;TZID=Australia/Sydney:20260615T140000
DTSTAMP:20260601T145743
CREATED:20260601T020224Z
LAST-MODIFIED:20260601T021959Z
UID:5173-1781528400-1781532000@spds.sydney.edu.au
SUMMARY:Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Long Yuan\, Johns Hopkins University \n\n\n\nThis is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nIn imaging-based spatial transcriptomics\, transcript-to-cell assignment shapes downstream biological interpretation\, including cell typing\, ligand–receptor inference\, and niche characterization. However\, two-dimensional segmentation of volumetric tissue often yields mixed cellular profiles\, while cells without detected nuclei may be missed entirely\, affecting downstream analyses. We present TRACER\, a framework that refines cellular representations in imaging-based spatial transcriptomics by leveraging gene–gene coherence and spatial co-localization of transcripts observed directly in the data\, without requiring external annotations or reference atlases. TRACER resolves mixed cellular profiles and reconstructs partial cells whose nuclei are not detected\, enabling more complete representation of cells within tissue sections. We also introduce coherence-based metrics that quantify transcriptional purity and conflict\, enabling platform-agnostic benchmarking of segmentation quality. Across diverse platforms\, tissues\, and segmentation methodologies\, TRACER consistently improves the coherence of cellular profiles and the quality of downstream analyses. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLong Yuan\n\n\n\nLong Yuan is a PhD candidate in Immunology and an M.S.E. candidate in Computer Science at Johns Hopkins University. His research focuses on developing scalable machine learning and statistical methods for spatial and single-cell omics\, with applications in cancer biology and immune-metabolic diseases. His work spans spatial multi-omics\, graph-based learning\, and multimodal data integration. As a member of the Break Through Cancer GBM and Data Science TeamLab\, he develops computational approaches for integrating and analyzing large-scale spatial omics datasets.Find out more on LinkedIn and X:https://www.linkedin.com/in/long-yuan-3a8b953aa/@Long_et_al
URL:https://spds.sydney.edu.au/event/reconstructing-biologically-coherent-cellular-profiles-from-imaging-based-spatial-transcriptomics/
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