Back

Imaging Intrinsic Stochastic Magnetic Fluctuations in Living Cells

Lin, W.; Ding, T.; Bao, C.; Miao, Y.; Zhou, J.; Wei, Z.; Jia, S.; Fan, C.; Liang, L.

2026-03-25 biophysics
10.64898/2026.03.23.713642 bioRxiv
Show abstract

SignificanceThis work establishes a probabilistic magnetometry framework for detecting weak stochastic magnetic fluctuations at the nanoscale, which provides the first quantitative access to intrinsic magnetic activity in living cells. Weak stochastic magnetic fluctuations at nanoscale are difficult to quantify. In living cells, ionic transport and molecular currents generate electromagnetic activity whose magnetic component is likewise nanoscale, weak, stochastic, and rapidly varying and has therefore remained experimentally inaccessible. Here we introduce Bio-Spin Probabilistic Inference (BISPIN), a digital statistical framework that can quantify weak, stochastic magnetic fluctuations at the nanoscale. Using threshold-resolved signals from enhanced nitrogen-vacancy quantum sensors, BISPIN converts unstable analog magnetic readouts into statistically convergent digital observables and infers fluctuation strength through probabilistic modeling, enabling robust quantification under random sensor orientations and biological heterogeneity within the experimental bandwidth. Applied to living cells, this approach distinguishes live from fixed cells, resolves agonist-induced activation, and maps subcellular variations in magnetic fluctuation strength. By providing the first quantitative access to intrinsic stochastic magnetic fluctuations in living cells, this work establishes a probabilistic magnetometry framework for cellular electrodynamics and opens a new magnetic dimension of cellular phenotyping for bio-spin omics.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 3%
14.1%
2
Nature Communications
4913 papers in training set
Top 19%
9.9%
3
Neuron
282 papers in training set
Top 2%
8.3%
4
Science
429 papers in training set
Top 4%
7.0%
5
Cell Systems
167 papers in training set
Top 2%
6.2%
6
Nature Physics
39 papers in training set
Top 0.3%
4.8%
50% of probability mass above
7
Cell
370 papers in training set
Top 4%
4.8%
8
eLife
5422 papers in training set
Top 20%
4.2%
9
Nature
575 papers in training set
Top 6%
4.1%
10
Nature Methods
336 papers in training set
Top 3%
3.5%
11
Science Advances
1098 papers in training set
Top 6%
3.5%
12
Cell Reports
1338 papers in training set
Top 21%
2.0%
13
Physical Review X
23 papers in training set
Top 0.2%
2.0%
14
Nature Neuroscience
216 papers in training set
Top 4%
1.7%
15
ACS Nano
99 papers in training set
Top 2%
1.6%
16
Nucleic Acids Research
1128 papers in training set
Top 13%
1.3%
17
Advanced Science
249 papers in training set
Top 13%
1.3%
18
Nature Biotechnology
147 papers in training set
Top 6%
1.2%
19
Biophysical Journal
545 papers in training set
Top 4%
1.2%
20
PLOS Computational Biology
1633 papers in training set
Top 21%
0.9%
21
Communications Physics
12 papers in training set
Top 0.5%
0.8%
22
ACS Photonics
13 papers in training set
Top 0.4%
0.7%
23
Scientific Reports
3102 papers in training set
Top 76%
0.7%
24
Molecular Cell
308 papers in training set
Top 10%
0.7%
25
PNAS Nexus
147 papers in training set
Top 2%
0.7%
26
Nature Biomedical Engineering
42 papers in training set
Top 2%
0.7%
27
iScience
1063 papers in training set
Top 35%
0.7%
28
Nature Microbiology
133 papers in training set
Top 5%
0.6%
29
Nano Letters
63 papers in training set
Top 3%
0.6%
30
Journal of the American Chemical Society
199 papers in training set
Top 6%
0.6%