Back

ColorSTEM: An easy and convenient approach to Multicolor Electron Microscopy of Labeled Biological Specimens

Ramachandra, R.; Mackay, M. R.; Hu, J.; Shone, T. M.; Flores, M. V.; Ramos, I. A.; Peltier, S. T.; Adams, S. R.; Ellisman, M. H.

2025-12-17 cell biology
10.64898/2025.12.15.694448 bioRxiv
Show abstract

A new method for Multicolor Electron Microscopy (EM) using scanning transmission EM, ColorSTEM has been developed to increase the sensitivity, instrument availability and ease of use compared to that previously developed using electron energy loss spectroscopy and energy filtered transmission EM. Both methods paint multiple subcellular complexes with different colors, enabled by the localized and targeted deposition of diaminobenzidine (DAB) conjugated to different metals, which provides the differentiating signal from each other. With ColorSTEM, elastic scattering that is dependent upon atomic weight (Z-contrast) has far greater signals. Using deposited DAB with copper or gold to label subcellular components, Z-contrast imaging generates a pseudo-elemental map, resulting from differences in the grayscale of the conventional STEM images.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Scientific Reports
○ 3612 papers in training set
Top 2%
13.2%
Similar papers in this journal
2
Journal of Microscopy
○ 20 papers in training set
Top 0.1%
10.0%
Similar papers in this journal
3
Journal of Structural Biology
○ 64 papers in training set
Top 0.1%
9.9%
Similar papers in this journal
4
Nature Communications
○ 5641 papers in training set
Top 20%
8.1%
Similar papers in this journal
5
PLOS ONE
● 5266 papers in training set
Top 29%
4.9%
Similar papers in this journal
6
Communications Biology
○ 993 papers in training set
Top 5%
3.3%
7
eLife
● 5828 papers in training set
Top 34%
3.3%
50% of probability mass above
8
Journal of Structural Biology: X
○ 17 papers in training set
Top 0.1%
3.2%
9
Journal of Cell Biology
● 392 papers in training set
Top 2%
2.8%
10
Cell Reports Methods
○ 165 papers in training set
Top 1%
2.2%
11
Advanced Biology
○ 29 papers in training set
Top 0.2%
1.9%
12
Science Advances
● 1243 papers in training set
Top 19%
1.8%
13
Nature Methods
○ 385 papers in training set
Top 4%
1.8%
14
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 27%
1.7%
15
ACS Applied Materials & Interfaces
● 39 papers in training set
Top 0.7%
1.4%
16
ACS Nano
● 113 papers in training set
Top 2%
1.2%
17
iScience
○ 1154 papers in training set
Top 25%
1.2%
18
Advanced Science
○ 286 papers in training set
Top 7%
1.1%
19
International Journal of Molecular Sciences
○ 494 papers in training set
Top 12%
1.1%
20
Nano Letters
● 71 papers in training set
Top 1%
1.0%
21
Structure
○ 193 papers in training set
Top 2%
1.0%
22
Journal of Visualized Experiments
● 34 papers in training set
Top 0.5%
0.9%
23
Microscopy and Microanalysis
◐ 12 papers in training set
Top 0.1%
0.9%
24
ACS Chemical Biology
● 167 papers in training set
Top 2%
0.9%
25
Analytical Chemistry
● 218 papers in training set
Top 2%
0.9%
26
Nanoscale Advances
● 15 papers in training set
Top 0.3%
0.9%
27
Small
○ 78 papers in training set
Top 1%
0.9%
28
Journal of the American Chemical Society
● 217 papers in training set
Top 3%
0.9%
29
Nature Protocols
○ 33 papers in training set
Top 0.6%
0.6%
30
Advanced Materials Interfaces
○ 10 papers in training set
Top 0.2%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.