Quantitative In-Cell Localization Microscopy: From Single-Molecule Localizations to Mechanistic Inference.
- 1Department of Chemistry, University of Houston, Houston, Texas 77204, United States.
Abstract
Quantitative in-cell localization microscopy can reveal nanoscale molecular organization, but the biological strength of a localization-derived claim depends on more than localization precision. Blinking, incomplete labeling, molecular motion, background heterogeneity, and analysis choices can bias apparent clustering, copy numbers, stoichiometries, and molecular states without obvious defects in reconstructed images. This tutorial review presents a claims ladder, a practical framework for evaluating which claims localization data can support. Its four rungs separate localization metrology, spatial organization, molecular-state inference, and mechanistic interpretation. This structure clarifies what is measured directly, what is inferred through calibrated models, and what requires perturbation-based support. We discuss how quality control, null baselines, parameter robustness tests, calibration, state-identifiability analysis, confounder auditing, and replication-aware inference help prevent overinterpretation as claims escalate from localization lists to spatial patterns, molecular states, and mechanisms. A worked CTR1/single-molecule neighbor density (smND) example illustrates how calibrated localization statistics can be used to infer oligomeric-state changes and constrain mechanistic interpretation while preserving the distinction among measurement, model-based inference, and causal explanation. This framework is intended to help researchers align localization-derived conclusions with transparent, rigorous, and reproducible evidence.
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BibTeX
@article{chen2026quantitative,
title = {{Quantitative In-Cell Localization Microscopy: From Single-Molecule Localizations to Mechanistic Inference.}},
author = {Wenkai Chen and Shudan Yan and Luiz B Fernandez and Guangjie Yan and Tai-Yen Chen},
journal = {Chemical \& biomedical imaging},
year = {2026},
month = sep,
doi = {10.1021/cbmi.6c00156},
url = {https://europepmc.org/article/MED/42953705},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Quantitative In-Cell Localization Microscopy: From Single-Molecule Localizations to Mechanistic Inference.
AU - Wenkai Chen
AU - Shudan Yan
AU - Luiz B Fernandez
AU - Guangjie Yan
AU - Tai-Yen Chen
PY - 2026
DA - 2026/09/07
JO - Chemical & biomedical imaging
DO - 10.1021/cbmi.6c00156
UR - https://europepmc.org/article/MED/42953705
AB - Quantitative in-cell localization microscopy can reveal nanoscale molecular organization, but the biological strength of a localization-derived claim depends on more than localization precision. Blinking, incomplete labeling, molecular motion, background heterogeneity, and analysis choices can bias apparent clustering, copy numbers, stoichiometries, and molecular states without obvious defects in reconstructed images. This tutorial review presents a claims ladder, a practical framework for evaluating which claims localization data can support. Its four rungs separate localization metrology, spatial organization, molecular-state inference, and mechanistic interpretation. This structure clarifies what is measured directly, what is inferred through calibrated models, and what requires perturbation-based support. We discuss how quality control, null baselines, parameter robustness tests, calibration, state-identifiability analysis, confounder auditing, and replication-aware inference help prevent overinterpretation as claims escalate from localization lists to spatial patterns, molecular states, and mechanisms. A worked CTR1/single-molecule neighbor density (smND) example illustrates how calibrated localization statistics can be used to infer oligomeric-state changes and constrain mechanistic interpretation while preserving the distinction among measurement, model-based inference, and causal explanation. This framework is intended to help researchers align localization-derived conclusions with transparent, rigorous, and reproducible evidence.
SN - 2832-3637
ER -