Imaging large fields-of-view at high resolution in cryo-ET with square beam montaging

Eugene Y.D. Chua, Hamidreza Rahmani, James Zhen, Fabian Eisenstein, Yong Hyun Song, Jake D. Johnston, Hanyu Wang, Lambertus M. Alink, Mykhailo Kopylov, Chi-Min Ho, Danielle A Grotjahn, Alex de Marco

CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets

Axel Levy, Rishwanth Raghu, J. Ryan Feathers, Michal Grzadkowski, Frédéric Poitevin, Jake D. Johnston, Francesca Vallese, Oliver Biggs Clarke, Gordon Wetzstein, Ellen D. Zhong

Cryo-ET reveals thein situarchitecture of the polar tube invasion apparatus from microsporidian parasites

Mahrukh Usmani, Nicolas Coudray, Margot Riggi, Rishwanth Raghu, Harshita Ramchandani, Daija Bobe, Mykhailo Kopylov, Ellen D. Zhong, Janet H. Iwasa, Damian C. Ekiert, Gira Bhabha

Jake Johnston

Machine Learning Center

Simons Machine Learning CenterThe Simons Machine Learning Center (SMLC) is a dedicated machine learning research group within NYSBC. The SMLC develops computational methods and software that improve how cryo-EM and cryo-ET data are collected, processed, and interpreted, with a focus on making structural biology workflows more automated, data-efficient, and informative.SMLC research applies modern machine learning, computer vision, and deep learning approaches to challenges across the cryo-EM pipeline. Areas of emphasis include automated microscope targeting and data collection; particle picking and object detection; image denoising; analysis of heterogeneous and flexible structures; tomogram and cellular segmentation; and models that integrate information from cryo-EM, cryo-ET, protein sequence, structure, and function. These methods help researchers extract more biological insight from complex, noisy, and information-rich datasets.By pairing algorithm development with the experimental scale of SEMC, the SMLC creates tools that can be tested on real cryo-EM and cryo-ET workflows and then shared with the broader structural biology community. This close connection between computation, instrumentation, and biological application allows the group to address practical bottlenecks in data acquisition and analysis while advancing new approaches for understanding biomolecular structure and function.

Cryo-EM Research

de Marco LabThe de Marco Lab, led by the Simons Electron Microscopy Center (SEMC) Director Alex de Marco, develops cryo-electron tomography (cryo-ET) into a routine, high-throughput tool for studying molecular structure directly inside cells. The group’s goal is to make in situ structural biology accessible and automated through every stage of the workflow—from sample preparation and cryo-FIB lamella fabrication through data collection and analysis—so that native cellular environments can be imaged with the scale and reproducibility that purified-sample cryo-EM already enjoys.The lab focuses on developing methods and instrumentation: automated cryo-FIB milling and correlative targeting; new devices and software for sample preparation and microscope control; and subtomogram-averaging pipelines that recover high-resolution structures from crowded cellular interiors. Building these tools within SEMC’s high-volume service environment lets the lab test new approaches on real user workflows and disseminate them to the broader structural biology community. These methods are applied to questions that require structural information across scales: how cells establish and maintain polarity; how molecular machines organize cell division; and how dynamic macromolecular assemblies change structure and composition during cellular processes. By pairing technology development with biological discovery, the group studies molecular events in their native context, bridging isolated complexes and intact cells.MethodsOptimizing cryo-EM data acquisition: SEMC researchers develop and refine strategies that improve the efficiency, reproducibility, and information content of cryo-EM and cryo-ET data collection. This includes work on acquisition workflows, dose management, targeting, throughput, and approaches that make high-resolution imaging more robust across different specimen types. Cryo-FIB methods and automation: The program advances cryo-focused ion beam methods for in situ structural biology, including lamella preparation, lift-out workflows, plasma-FIB approaches, correlative targeting, and automation designed to increase throughput while preserving native cellular architecture. Sample preparation and freezing technologies: SEMC develops and applies preparation workflows that support a wide range of biological samples, including plunge freezing, blotless vitrification, high-pressure freezing, cryo-CLEM-guided targeting, micropatterning, and other approaches that improve specimen quality for single-particle cryo-EM and cellular cryo-ET. Improving cryo-EM and cryo-ET data analysis: Research in data analysis focuses on extracting biological information from heterogeneous and flexible samples. Areas of emphasis include single-particle analysis of flexible RNA and ribonucleoprotein assemblies, methods for resolving conformational heterogeneity, subtomogram and tomographic analysis, and segmentation approaches that connect molecular structure to cellular context. Ultra-sensitive proteomics and multiomics approaches: SEMC also integrates structural biology with ultra-sensitive proteomics and emerging multiomics strategies. These approaches enable researchers to connect molecular structure, protein composition, and cellular state, supporting studies where limited sample material or single-cell measurements are essential.Biological ApplicationsSEMC research applies these methods to biological questions that require structural information across scales, from purified molecular assemblies to native cellular environments. Current application areas include understanding how cells establish and maintain polarity, how molecular machines organize cell division, and how dynamic macromolecular assemblies change structure and composition during cellular processes.

Correlative Light and Electron Microscopy (CLEM)

Correlative light and electron microscopy (CLEM) combines the strengths of both modalities: fluorescence microscopy’s specificity and cellular context with electron microscopy’s structural resolution. Cryo-CLEM extends this to vitrified specimens, eliminating chemical fixation and preserving native cellular organization while enabling precise, light-guided targeting of regions for high-resolution imaging.NYSBC’s cryo-CLEM facility integrates advanced confocal and super-resolution light microscopy with cryo-EM, enabling  scientists to identify structures of interest by fluorescence, then image them at molecular resolution. The approach is particularly powerful for projects where cellular heterogeneity or spatial context is essential, including studies of viral infection, organelle architecture, or protein organization within native environments. NYSBC staff helps researchers develop their proficiency across the full platform and collaborates with them to customize integrated workflows.

Electron Microscopy

One of the world’s largest cryo-electron microscopy fleets, spanning single-particle imaging, tomography, and in situ methods.

Cryo-Electron Tomography

Cryo-electron tomography (cryo-ET) extends structural biology into the cellular environment, revealing how macromolecular assemblies are organized and function within intact cells and tissues. The technique captures three-dimensional slices through vitrified specimens, preserving native cellular architecture without chemical fixation or heavy-metal staining.NYSBC supports cryo-ET across two complementary workflows. On-grid tomography works with purified or partially enriched samples, enabling focused structural studies with moderate specimen complexity. In situ approaches—combining cryo-FIB lamella preparation with tomographic imaging—target specific cellular structures directly, from organelles to intact subcellular compartments. Both workflows benefit from integrated sample preparation, correlative light microscopy guidance, and expert staff support.