Evidence map›Paper›PMID 37066179›Full record

ArticlebioRxiv : the preprint server for biology2023

BNP-Track: A framework for multi-particle superresolved tracking.

Lance W Q Xu, Ioannis Sgouralis, Zeliha Kilic, Steve Pressé

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Lance W Q XuCenter for Biological Physics, Department of Physics, Arizona State University, Tempe, AZ 85287, USA.
Ioannis SgouralisDepartment of Mathematics, University of Tennessee, Knoxville, TN 37996, USA.
Zeliha KilicSingle-Molecule Imaging Center, Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
Steve PresséCenter for Biological Physics, Department of Physics, Arizona State University, Tempe, AZ 85287, USA.

Funding

A Bayesian nonparametric approach to superresolved tracking of multiple molecules inside living cellsR01GM130745 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI PRESSE, STEVE · 2019 to 2023
$1.5M
Theoretical Models of Single Molecule Dynamics from Minimal Photon NumbersR01GM134426 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI PRESSE, STEVE · 2019 to 2022
$1.2M
NIGMS NIH HHS R01 GM130745NIGMS NIH HHS R01 GM134426
6 · The paper itself

Abstract

When tracking fluorescently labeled molecules (termed "emitters") under widefield microscopes, point spread function overlap of neighboring molecules is inevitable in both dilute and especially crowded environments. In such cases, superresolution methods leveraging rare photophysical events to distinguish static targets nearby in space introduce temporal delays that compromise tracking. As we have shown in a companion manuscript, for dynamic targets, information on neighboring fluorescent molecules is encoded as spatial intensity correlations across pixels and temporal correlations in intensity patterns across time frames. We then demonstrated how we used all spatiotemporal correlations encoded in the data to achieve superresolved tracking. That is, we showed the results of full posterior inference over both the number of emitters and their associated tracks simultaneously and self-consistently through Bayesian nonparametrics. In this companion manuscript we focus on testing the robustness of our tracking tool, BNP-Track, across sets of parameter regimes and compare BNP-Track to competing tracking methods in the spirit of a prior Nature Methods tracking competition. We explore additional features of BNP-Track including how a stochastic treatment of background yields greater accuracy in emitter number determination and how BNP-Track corrects for point spread function blur (or "aliasing") introduced by intraframe motion in addition to propagating error originating from myriad sources (such as criss-crossing tracks, out-of-focus particles, pixelation, shot and camera artefact, stochastic background) in posterior inference over emitter numbers and their associated tracks. While head-to-head comparison with other tracking methods is not possible (as competitors cannot simultaneously learn molecule numbers and associated tracks), we can give competing methods some advantages in order to perform approximate head-to-head comparison. We show that even under such optimistic scenarios, BNP-Track is capable of tracking multiple diffraction-limited point emitters conventional tracking methods cannot resolve thereby extending the superresolution paradigm to dynamical targets.

Identifiers

PMID37066179
PMCPMC10104013

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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.