Quantitative Data Analysis in Single-Molecule Localization Microscopy

Wu, Yule; Tschanz, Aline; Krupnik, Leonard; Ries, Jonas

doi:10.1016/j.tcb.2020.07.005

Cited by 53 publications

(49 citation statements)

References 87 publications

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“…(8) The emitted photons in each frame were determined as a random Poisson variable with a mean corresponding to the average brightness in the frame. (9) For each frame, we calculated the CRLB (Cramér-Rao lower bound) in x, y and z from the number of photons and the background photons 41 . (10) This error was added to the true x, y and z positions of the fluorophores as normally distributed random values with a variance corresponding to the respective calculated CRLB.…”

Section: Simulationmentioning

confidence: 99%

“…The primary data in SMLM, but also in the new MINFLUX 8 super-resolution technology, are not the reconstructed images, but a list of coordinates of fluorophores, often with additional information such as an estimate of the localization uncertainty. Thus, algorithms that directly use these coordinates can exploit this additional information and can produce more accurate and robust results 9 . These algorithms can be assigned to several classes 9 : Spatial descriptive statistics , such as pair-correlation 10 or Ripley’s K-function 11 , inform on the clustering state and cluster sizes in specific regions of interest.…”

Section: Introductionmentioning

confidence: 99%

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Maximum-likelihood model fitting for quantitative analysis of SMLM data

Hoess

Tschanz

et al. 2021

Preprint

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Quantitative analysis is an important part of any single-molecule localization microscopy (SMLM) data analysis workflow to extract biological insights from the coordinates of the single fluorophores, but current approaches are restricted to simple geometries or do not work on heterogenous structures. Here, we present LocMoFit (Localization Model Fit), an open-source framework to fit an arbitrary model directly to the localization coordinates in SMLM data. Using maximum likelihood estimation, this tool extracts the most likely parameters for a given model that best describe the data, and can select the most likely model from alternative models. We demonstrate the versatility of LocMoFit by measuring precise dimensions of the nuclear pore complex and microtubules. We also use LocMoFit to assemble static and dynamic multi-color protein density maps from thousands of snapshots. In case an underlying geometry cannot be postulated, LocMoFit can perform single-particle averaging of super-resolution structures without any assumption about geometry or symmetry. We provide extensive simulation and visualization routines to validate the robustness of LocMoFit and tutorials based on example data to enable any user to increase the information content they can extract from their SMLM data.

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Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maximum-likelihood model fitting for quantitative analysis of SMLM data

Hoess

Tschanz

et al. 2021

Preprint

Self Cite

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“…These range from stand-alone programs such as DBScan (Ester et al, 1996) to the use of machine learning (Williamson et al, 2020). Both the principles of SMLM and data analyses have been reviewed recently (Wu et al, 2020). The organization of other molecules including Lck, ZAP-70, Grb2, and SLP-76 in microclusters has also been studied (Lillemeier et al, 2010;Purbhoo et al, 2010;Hsu and Baumgart, 2011;Sherman et al, 2011;Rossy et al, 2013;Neve-Oz et al, 2015).…”

Section: Spatial Organization Of Microclustersmentioning

confidence: 99%

Microclusters as T Cell Signaling Hubs: Structure, Kinetics, and Regulation

Balagopalan

Raychaudhuri

Samelson

2021

Front. Cell Dev. Biol.

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When T cell receptors (TCRs) engage with stimulatory ligands, one of the first microscopically visible events is the formation of microclusters at the site of T cell activation. Since the discovery of these structures almost 20 years ago, they have been studied extensively in live cells using confocal and total internal reflection fluorescence (TIRF) microscopy. However, due to limits in image resolution and acquisition speed, the spatial relationships of signaling components within microclusters, the kinetics of their assembly and disassembly, and the role of vesicular trafficking in microcluster formation and maintenance were not finely characterized. In this review, we will summarize how new microscopy techniques have revealed novel insights into the assembly of these structures. The sub-diffraction organization of microclusters as well as the finely dissected kinetics of recruitment and disassociation of molecules from microclusters will be discussed. The role of cell surface molecules in microcluster formation and the kinetics of molecular recruitment via intracellular vesicular trafficking to microclusters is described. Finally, the role of post-translational modifications such as ubiquitination in the downregulation of cell surface signaling molecules is also discussed. These results will be related to the role of these structures and processes in T cell activation.

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“…The super-resolution techniques include single-molecule localization methods (SMLM), such as PALM, STORM, and PAINT. The super-resolution techniques are achieved by isolating emitters and fitting images with the point-spread function to the fluorescence of single molecules [199,200]. Point accumulation for imaging in nanoscale topography (PAINT), which is the one of SMLM, allows capturing several points of stochastic single-molecule fluorescence emitted by molecular adsorption/absorption and photobleaching/desorption by using fast and transient fluorescence dyes.…”

Section: Single-molecule Imaging For Dna Origamimentioning

confidence: 99%

DNA Manipulation and Single-Molecule Imaging

2021

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DNA replication, repair, and recombination in the cell play a significant role in the regulation of the inheritance, maintenance, and transfer of genetic information. To elucidate the biomolecular mechanism in the cell, some molecular models of DNA replication, repair, and recombination have been proposed. These biological studies have been conducted using bulk assays, such as gel electrophoresis. Because in bulk assays, several millions of biomolecules are subjected to analysis, the results of the biological analysis only reveal the average behavior of a large number of biomolecules. Therefore, revealing the elementary biological processes of a protein acting on DNA (e.g., the binding of protein to DNA, DNA synthesis, the pause of DNA synthesis, and the release of protein from DNA) is difficult. Single-molecule imaging allows the analysis of the dynamic behaviors of individual biomolecules that are hidden during bulk experiments. Thus, the methods for single-molecule imaging have provided new insights into almost all of the aspects of the elementary processes of DNA replication, repair, and recombination. However, in an aqueous solution, DNA molecules are in a randomly coiled state. Thus, the manipulation of the physical form of the single DNA molecules is important. In this review, we provide an overview of the unique studies on DNA manipulation and single-molecule imaging to analyze the dynamic interaction between DNA and protein.

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Quantitative Data Analysis in Single-Molecule Localization Microscopy

Cited by 53 publications

References 87 publications

Maximum-likelihood model fitting for quantitative analysis of SMLM data

Maximum-likelihood model fitting for quantitative analysis of SMLM data

Microclusters as T Cell Signaling Hubs: Structure, Kinetics, and Regulation

DNA Manipulation and Single-Molecule Imaging

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