Biological Tutorial

X40 – Computational Microscopy: Label-free Imaging

Organizer: Eduardo Rosa-Molinar, Ph.D., Director, Washington University Center for Cellular Imaging, Professor, Cell Biology, Physiology, and Neuroscience, Washington University School of Medicine in St Louis.

Speaker: Ivan Ivanov, Ph.D., Senior Research and Development Engineer, Chan Zuckerberg Biohub San Francisco.

Computational microscopy, i.e. the joint design of the imaging system and the reconstruction algorithms, has allowed us to circumvent trade-offs imposed by conventional microscopy methods. Computational microscopy methods (such as cryo-ET, super-resolution fluorescence, and quantitative label-free microscopy) enable new mechanistic studies or high-throughput screens. These methods build on a long history of qualitative imaging techniques that create contrast in largely transparent samples using known light-matter interactions. In this tutorial session, I will introduce label-free phase and polarization imaging through demonstrations and explanation of the underlying physical principles. I will discuss classical microscopy contrast methods and review how modern computational microscopy techniques build upon them. Lastly, I will demonstrate how our lab combines quantitative label-free phase and polarization imaging with fluorescence microscopy to study the dynamics of specific molecules of interest in the context of the overall architecture of live cells and organisms. Through large collaborative efforts at the Biohub, we aim to create an intracellular disease dashboard highlighting how cells respond to viral infection and to map the dynamic emergence of cell identity during development.

Physical Sciences Tutorial

X41 – Diffraction Contrast Microscopy: Then and Now

Organizer: Sarshad Rommel, University of Connecticut

Speaker: Babu Viswanathan, Research Professor, Department of Materials and Engineering, Center for Electron Microscopy and Analysis, University of Ohio

This tutorial session will cover an overview of diffraction contrast microscopy (DCM), covering the principles behind DCM and practical applications of the technique in regard to structural analysis and investigations of crystalline defects. 

Cross-Topic (Biological & Physical Sciences) Tutorial

X42 – What’s next for imaging?

Organizer: Eduardo Rosa-Molinar, Ph.D., Director, Washington University Center for Cellular Imaging, Professor, Cell Biology, Physiology, and Neuroscience, Washington University School of Medicine in St Louis.

Speaker: David Grunwald, Ph.D., Associate Professor, RNA Therapeutics Institute, University of Massachusetts Medical School.

Microscopy has provided extraordinary insight into the structure and function of human tissues and, with the rate of technical advancements, will undoubtedly lead to further unimaginable biomedical breakthroughs.

However, the next advance in capability may well result from standardization of language describing microscopes and their settings as technical instruments. Moreover, such standardization will respond to increasing calls for reproducibility, reusability, interpretability, equitability, and accessibility.

Standardization will lead to breakthroughs through: a) making scientific knowledge and technical advancements more accessible; b) streamlining large-scale experiments across multiple laboratories; and c) empowering Artificial Intelligence, Machine Learning and Large Language Model (AI/ML/LLM) approaches to extract crucial insights from combined imaging data. The proposed tutorial aims to provide a framework for pursuing and achieving standardization.

First, as a starting point, this tutorial will provide an overview of pertinent projects and initiatives in the bio and biomedical field, including: the NIH-funded 4D NucleomeHuBMAP, and SenNet projects, ABRF, BioImaging North America (BINA), German BioImaging (GerBI), the Open Microscopy Environment Consortium (OME), founding GIDE, the consortium for quality control and reproducibility in light microscopy (QUAREP-LiMi) and REMBI.

Second, it will explore a) commonalities between disciplines; b) impacts on needs for updates to training environments; c) infrastructure needs with respect to data formats, data exchange ability, and technical requirements for metadata schema; and d) implementation limits.

Third, it will raise questions regarding next steps, especially with respect to quality control, performance assessment and their role in image data analysis and reusability.

Finally, it will discuss the potential impacts from data management initiatives being run without formalized USA representation, but supported by other governments[1], and aim to assess the needs and opportunities for a combined push from LM and EM communities in the USA to raise attention to these new frontiers for imaging sciences.


[1] Through substantial national funding approaches for instance in Australia, the Euro-Zone, Germany, and Japan.