US-12620100-B2 - Method for detecting spatial coupling

US12620100B2US 12620100 B2US12620100 B2US 12620100B2US-12620100-B2

Abstract

Method for detecting spatial coupling comprising the steps of: a. providing a set of data, b. identifying and segmenting a first and a second sets of objects of interest, wherein the objects of the second set are assimilated to punctual objects, c. determining, using a level set function, an expected number of objects of the second set present within a specified range of distances to at least one given object of the first set in case there were no interactions between said at least one given object of the first set and the objects of the second set, d. determining, using a level set function, an actual number of objects of the second set within the same range of distances to the at least one given object of the first set, and e. comparing said expected amount and said determined amount.

Inventors

Suvadip MUKHERJEE
Thibault LAGACHE
Jean-Christophe Olivo-Marin

Assignees

INSTITUT PASTEUR
Centre National de la Recherche Scientifique—CNRS

Dates

Publication Date: 20260505
Application Date: 20210611
Priority Date: 20200612

Claims (13)

1 . Method for detecting spatial coupling comprising the steps of: a. providing a set of data, b. identifying and segmenting a first and a second sets of objects of interest, wherein the objects of the second set are assimilated to punctual objects, c. defining a plurality of distance-based analytical zones around at least two given objects of the first set by using a level set function derived from a boundary of the union of the objects of the first set, and determining an expected number of objects of the second set present within said analytical zones, wherein said expected number is determined under an assumption of no interactions between said given object of the first set and the objects of the second set, d. determining, using a level set function, an actual number of objects of the second set within at least one of said analytical zones, and e. comparing said expected amount and said determined amount.
2 . The method according to claim 1 wherein the set of data is a set of imaging data.
3 . The method according to claim 1 wherein the spatial coupling to be identified is a molecular spatial coupling between molecular objects of interest.
4 . The method according to claim 3 wherein the provided set of data includes a biological tissues imaging data.
5 . The method according to claim 3 wherein the provided set of data includes microscopy imaging data.
6 . The method according to claim 1 wherein steps c and d are repeated for all objects of the first set.
7 . The method according to claim 1 wherein steps c and d are performed for several different sets of analytical zones.
8 . The method according to claim 7 wherein the different sets of analytical zones are not consecutive.
9 . The method according to claim 1 wherein the data comprise spatial coordinates and/or temporal coordinates.
10 . The method according to claim 1 wherein the first and second sets of objects of interest comprise punctual objects and/or regions.
11 . A non-transitory computer program product comprising a non-transitory computer-readable medium storing code configured to, when executed by a processor or an electronic control unit, perform the method according to claim 1 .
12 . The method of claim 4 , wherein the biological tissues imaging data is medical imaging data.
13 . The method of claim 9 , wherein the coordinates are bidimensional or tridimensional spatial coordinates, and/or temporal coordinates.

Description

The present invention relates to the field of spatial coupling and image processing which finds exemplary application in the medical field or in the fields of fluorescent microscopy, super resolution imaging, and histopathology. The analysis of the spatial distribution of molecules and organelles in bioimaging remains a gold-standard for understanding cellular processes at the molecular level. Different imaging modalities can be used at different spatial scales, ranging from optical coherence tomography in histological samples to fluorescence and electron microscopy in molecular cell biology, to name a few. In fluorescence microscopy, the spatial proximity of molecules labeled with different colors (green and red typically) is usually quantified through their signal overlap or correlation (manifested as a yellow signal typically). However, developments in molecule labeling, optics and mathematical imaging have led to significant increase in spatial resolution (e.g. structured illumination and single-molecule localization microscopy (SMLM)), and made obsolete standard colocalization techniques. Indeed, when molecules are not directly described by their localization's estimate such as in SMLM, the microscope point spread function (PSF) is significantly reduced and spots of close molecules are not overlapping anymore, or only partially. Therefore, object-based methods that analyze the spatial relations between the objects' positions (single molecule localization or spots' center of mass) have been introduced over the past few years. To compute the spatial proximity (or coupling) between objects' spatial distributions, a part of object-based methods are dedicated to SMLM and define clusters of molecules localizations with Voronoi tessellation or any other density-based clustering methods, before measuring the overlap between clusters. The other object-based methods, that can be used for any type of microscope, directly measure the distances between all individual objects (localizations or spots' centers) and summarize distances' information with the Ripley's K function or any other second-order spatial statistic. In “Mapping molecular assemblies with fluorescence microscopy and object-based spatial statistics,”, Lagache et al. Nat Commun, vol. 9, no. 1, p. 698, 02 2018, a Ripley-based statistic is used to show the statistical significance of the measured spatial coupling between individual objects is computed. For this, the expected Ripley statistic is characterized analytically under the null hypothesis of complete spatial randomness of objects within the field of view (FOV). Statistical analysis allowed to map all individual coupled objects, i.e. those that are significantly close given the geometry of the FOV. By reducing the object's information to its spatial position, previous methods are not accurate when dealing with objects that are large and complex-in-shape. Typically, complex shapes arise when objects' size is larger than the PSF in standard fluorescence microscopy (e.g. cell edges, tubular organelles such as mitochondria or synaptic aggregates). Also, complex-shape objects can originate from localizations' clustering in SMLM. For the latter application, there has been little research to statistically measure the coupling of segmented clusters with single molecules' localizations. There is thus a need to provide a method to describe the spatial coupling between the underlying random point process and larger objects, so as to identify spatial coupling. An object of the present invention is therefore to provide a method for detecting spatial coupling comprising the steps of: a. providing a set of data, for example imaging data,b. identifying and segmenting a first and a second sets of objects of interest, wherein the objects of the second set are assimilated to punctual objects,c. determining an expected number of objects of the second set present within a specified range of distances to at least one given object of the first set, and preferably a parameter allowing to estimate the variation of that expected number such as the variance, in case there were no interactions (for example as defined by a suitable null hypothesis on the spatial distribution of the objects in the second set with regard to the first set) between said at least one given object of the first set and the objects of the second set,d. determining an actual number of objects of the second set within the same range of distances to the at least one given object of the first set, ande. comparing said expected amount and said determined amount. The comparison of step e can allow estimating the significant number of objects of the second set which are statistically above the expected accumulation for the aforementioned non-interacting case. “Spatial coupling” or “spatial association” is to be understood as the colocalization of statistically coupled points. Although the steps are presented in a specific order, the invention is by no mean