DRIFT-EM enables direct wafer retrieval of ultrathin serial sections for large-volume electron microscopy

Large-volume serial electron microscopy (vEM) has transformed neuroscience by enabling reconstructions of neural circuits at synaptic resolution. Approaches to collecting libraries of ultrathin sections required for producing vEM datasets have been devised (e.g., automated tape-collecting ultramicrotome [ATUM], MagC, and GAUSS-EM), which has advanced the scalability of vEM in distinct ways, yet widespread adoption remains constrained by cost, hardware requirements, or infrastructure demands. Here, we introduce direct retrieval by ionizer-facilitated transfer for electron microscopy (DRIFT-EM), a low-cost and easily implemented platform designed to broaden access to direct wafer-based collection of ultrathin sections. Off-the-shelf static ionizers clear newly cut sections from the knife edge, a thermoplastic boundary confines sections, and an open-source software pipeline automates region of interest identification to guide imaging. DRIFT-EM achieves section packing densities comparable to established magnetic methods, with minimal setup cost and maintenance. Its modular style, 3D-printed components, and open design facilitate integration with other vEM workflows, helping lower barriers to creating connectomes.

Authors

Institutions

Publication Details

Journal
Cell Reports Methods
Published
2026-05-01
DOI
https://doi.org/10.1016/j.crmeth.2026.101429
Primary Topic
Advanced Electron Microscopy Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DRIFT-EM enables direct wafer retrieval of ultrathin serial sections for large-volume electron microscopy

Yaron Meirovitch, Joseph V. Gogola, Fuming Yang, Narayanan Kasthuri et al.
Cell Reports Methods
Advanced Electron Microscopy Techniques and Applications
article

DRIFT-EM enables direct wafer retrieval of ultrathin serial sections for large-volume electron microscopy

Yaron Meirovitch, Joseph V. Gogola, Fuming Yang, Narayanan Kasthuri, Jeff W. Lichtman, Kevin Boergens, Gregg Wildenberg, Nelson Medina
article en

Abstract

Large-volume serial electron microscopy (vEM) has transformed neuroscience by enabling reconstructions of neural circuits at synaptic resolution. Approaches to collecting libraries of ultrathin sections required for producing vEM datasets have been devised (e.g., automated tape-collecting ultramicrotome [ATUM], MagC, and GAUSS-EM), which has advanced the scalability of vEM in distinct ways, yet widespread adoption remains constrained by cost, hardware requirements, or infrastructure demands. Here, we introduce direct retrieval by ionizer-facilitated transfer for electron microscopy (DRIFT-EM), a low-cost and easily implemented platform designed to broaden access to direct wafer-based collection of ultrathin sections. Off-the-shelf static ionizers clear newly cut sections from the knife edge, a thermoplastic boundary confines sections, and an open-source software pipeline automates region of interest identification to guide imaging. DRIFT-EM achieves section packing densities comparable to established magnetic methods, with minimal setup cost and maintenance. Its modular style, 3D-printed components, and open design facilitate integration with other vEM workflows, helping lower barriers to creating connectomes.

Cell Reports Methods
Argonne National Laboratory (US), Harvard University (US), University of Illinois Chicago (US), University of Chicago (US)
National Institute of Neurological Disorders and Stroke
Openalex Percentile: Top 7%
Advanced Electron Microscopy Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.