A density-based enrichment measure for assessing colocalization in single-molecule localization microscopy data

Ejdrup, Aske L; Lycas, Matthew D.; Lorenzen, Niels; Konomi, Ainoa; Herborg, Freja; Madsen, Kenneth L.; Gether, Ulrik

doi:10.1038/s41467-022-32064-y

Cited by 9 publications

(2 citation statements)

References 49 publications

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“…SEraster uses a two-step binarization approach to create binary presence/absence data for a given spatial omics dataset. Relative enrichment (RE) metric represents the ratio of observed to expected cell-type counts, and similar framework has been used in the context of single-molecule localization microscopy for assessing colocalization of two molecular species with Voronoï tessellation (Ejdrup et al, 2022). In SEraster, RE for each pixel, 𝑖, and category, 𝑗, i computed using the rasterized cell-type or cluster labels based on the following equation…”

Section: Supplementary Information Formentioning

confidence: 99%

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

Aihara,

Clifton,

Chen

et al. 2024

Preprint

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MotivationSpatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells.ResultsTo enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce resource requirements while maintaining high performance. We further integrate SEraster with existing analysis tools to characterize cell-type spatial cooccurrence. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as cooccurring cell-types that recapitulate expected organ structures.Availability and implementationSource code is available on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials athttps://JEF.works/SEraster.

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Section: Supplementary Information Formentioning

confidence: 99%

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

Aihara,

Clifton,

Chen

et al. 2024

Preprint

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show abstract

“…Identifying biomolecular identity, colocalizing partners, cellular localization, or the time point of a biological event is a challenge in fluorescence microscopy, necessitating specialized analysis, parallelized multicolor and often super-resolution imaging [35][36][37][38] . Such experimental design, specialized analysis and fluorescent tagging of biomolecular entities is substantially labor-and material intensive and risks impairing biological function by tagging 39 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning assisted single particle tracking for automated correlation between diffusion and function

Kæstel-Hansen,

de Sautu,

Saminathan

et al. 2023

Preprint

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Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone indicates that besides structure, motion encodes function at the molecular and subcellular level.

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SARS-CoV-2 booster vaccine dose significantly extends humoral immune response half-life beyond the primary series

Korosec,

Dick,

Moyles

et al. 2024

Preprint

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SARS-CoV-2 lipid nanoparticle mRNA therapeutics continue to be administered as the predominant therapeutic intervention to reduce COVID-19 disease pathogenesis. Quantifying the kinetics of the secondary immune response from subsequent doses beyond the primary series, and understanding how dose-dependent immune waning kinetics vary as a function of age, sex, and various comorbidities, remains an important question. We study anti-spike IgG waning kinetics in 152 individuals who received an mRNA-based primary series and a subset of 137 individuals who then received a booster dose. We find the booster dose elicits a 71-84\% increase in the median Anti-S half life over that of the primary series. We find the Anti-S half life for both primary series and booster doses drops as a function of increased year of age. However, we stress that although chronological age continues to be a good proxy for vaccine-induced humoral waning, immunosenescence is likely not the mechanism, rather, more likely the mechanism is related to the presence of noncommunicable diseases, which also accumulate with age, that affect immune regulation. We are able to independently reproduce recent observations that those with pre-existing asthma exhibit a stronger primary series humoral response to vaccination than compared to those that do not, and further find this result is sustained for the booster dose. Finally, via a single-variate Kruskal-Wallis Test we find no difference between male and female decay kinetics, however, a multivariate approach utilizing Lasso regression for feature selection reveals a statistically significant (p-value <10-3), albeit small, bias in favour of longer-lasting humoral immunity amongst males.

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A density-based enrichment measure for assessing colocalization in single-molecule localization microscopy data

Cited by 9 publications

References 49 publications

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

Deep learning assisted single particle tracking for automated correlation between diffusion and function

SARS-CoV-2 booster vaccine dose significantly extends humoral immune response half-life beyond the primary series

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