Evidence map›Paper›PMID 30599574›Full record

ArticleThe Review of scientific instruments2018

Theoretical analysis of spectral precision in spectroscopic single-molecule localization microscopy.

Ki-Hee Song, Biqin Dong, Cheng Sun, Hao F Zhang

Erratum issuedAbstract read
In one paragraph

Article in The Review of scientific instruments, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

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  7. Spectral precision improvement with demagnifying spectral images in spectroscopic nanoscopy.Journal of the Optical Society of America. A, Optics, image science, and vision · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Ki-Hee SongDepartment of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, USA.ORCID 0000000210772667
Biqin DongDepartment of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, USA.
Cheng SunDepartment of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, USA.
Hao F ZhangDepartment of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, USA.

Funding

Investigating oxygen metabolism in diabetic retinopathyR01EY026078 · NEI · NORTHWESTERN UNIVERSITY · PI SHEIBANI, NADER, ZHANG, HAO F · 2016 to 2020
$2.3M
Investigating nanoscale neuronal damages in early glaucoma towards clinical optical detectionR01EY029121 · NEI · NORTHWESTERN UNIVERSITY · PI LIU, XIAORONG, ZHANG, HAO F · 2018 to 2021
$2.0M
NEI NIH HHS R01 EY026078NEI NIH HHS R01 EY029121
6 · The paper itself

Abstract

Spectroscopic single-molecule localization microscopy (sSMLM) is a novel super-resolution imaging technology, which simultaneously records the nanoscopic location and the corresponding full emission spectrum of every stochastic single-molecule emission event. This spectroscopic imaging capability of sSMLM necessitates the establishment of a theoretical foundation of the newly introduced spectral precision and to guide the system design and optimization. Based on numerical simulation and analytical solution, we introduced such a theoretical model to analyze spectral precision by considering the main system parameters, including signal and background shot noises, readout noise, and the spectral calibration procedure. Using this model, we demonstrated the delicate balance among these parameters in achieving the optimal spectral precision and discovered that the best spectral precision can only be achieved at a particular system spectral dispersion. For example, with a given signal of 3000 photons and a readout noise of 2 e-, a system spectral dispersion of 1.6 nm/pixel is required for sSMLM to achieve the highest spectral precision of 1.31 nm.

Identifiers

PMID30599574
PMCPMC6289825

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.