Testing independence between two random sets for the analysis of colocalization in bioimaging

Extended correlation functions for spatial analysis of multiplex imaging data

Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.

A generative model to simulate spatiotemporal dynamics of biomolecules in cells

Generators of space-time dynamics in bioimaging have become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms. In this contribution, we leverage a stochastic model, called birth-death-move (BDM) point process, in order to generate joint dynamics of biomolecules in cells. This particle-based stochastic simulation method is very flexible and can be seen as a generalization of well-established standard particle-based generators. In comparison, our approach allows us: (1) to model a system of particles in motion, possibly in interaction, that can each possibly switch from a motion regime (e.g., Brownian) to another (e.g., a directed motion); (2) to take into account finely the appearance over time of new trajectories and their disappearance, these events possibly depending on the cell regions but also on the current spatial configuration of all existing particles. This flexibility enables to generate more realistic dynamics than standard particle-based simulation procedures, by for example accounting for the colocalization phenomena often observed between intracellular vesicles. We explain how to specify all characteristics of a BDM model, with many practical examples that are relevant for bioimaging applications. As an illustration, based on real fluorescence microscopy datasets, we finally calibrate our model to mimic the joint dynamics of Langerin and Rab11 proteins near the plasma membrane, including the well-known colocalization occurrence between these two types of vesicles. We show that the resulting synthetic sequences exhibit comparable features as those observed in real microscopy image sequences.

Multiscale microscopy to decipher plant cell structure and dynamics

New imaging methodologies with high contrast and molecular specificity allow researchers to analyze dynamic processes in plant cells at multiple scales, from single protein and RNA molecules to organelles and cells, to whole organs and tissues. These techniques produce informative images and quantitative data on molecular dynamics to address questions that cannot be answered by conventional biochemical assays. Here, we review selected microscopy techniques, focusing on their basic principles and applications in plant science, discussing the pros and cons of each technique, and introducing methods for quantitative analysis. This review thus provides guidance for plant scientists in selecting the most appropriate techniques to decipher structures and dynamic processes at different levels, from protein dynamics to morphogenesis.

In vivo characterization of Drosophila golgins reveals redundancy and plasticity of vesicle capture at the Golgi apparatus

The Golgi is the central sorting station in the secretory pathway and thus the destination of transport vesicles arriving from the endoplasmic reticulum and endosomes and from within the Golgi itself. Cell viability, therefore, requires that the Golgi accurately receives multiple classes of vesicle. One set of proteins proposed to direct vesicle arrival at the Golgi are the golgins, long coiled-coil proteins localized to specific parts of the Golgi stack. In mammalian cells, three of the golgins, TMF, golgin-84, and GMAP-210, can capture intra-Golgi transport vesicles when placed in an ectopic location. However, the individual golgins are not required for cell viability, and mouse knockout mutants only have defects in specific tissues. To further illuminate this system, we examine the Drosophila orthologs of these three intra-Golgi golgins. We show that ectopic forms can capture intra-Golgi transport vesicles, but strikingly, the cargo present in the vesicles captured by each golgin varies between tissues. Loss-of-function mutants show that the golgins are individually dispensable, although the loss of TMF recapitulates the male fertility defects observed in mice. However, the deletion of multiple golgins results in defects in glycosylation and loss of viability. Examining the vesicles captured by a particular golgin when another golgin is missing reveals that the vesicle content in one tissue changes to resemble that of a different tissue. This reveals a plasticity in Golgi organization between tissues, providing an explanation for why the Golgi is sufficiently robust to tolerate the loss of many of the individual components of its membrane traffic machinery.

Quantitative dSTORM super-resolution microscopy localizes Aurora kinase A/AURKA in the mitochondrial matrix

BACKGROUND INFORMATION

Mitochondria are dynamic organelles playing essential metabolic and signaling functions in cells. Their ultrastructure has largely been investigated with electron microscopy (EM) techniques. However, quantifying protein-protein proximities using EM is extremely challenging. Super-resolution microscopy techniques as direct stochastic optical reconstruction microscopy (dSTORM) now provide a fluorescent-based, quantitative alternative to EM. Recently, super-resolution microscopy approaches including dSTORM led to valuable advances in our knowledge of mitochondrial ultrastructure, and in linking it with new insights in organelle functions. Nevertheless, dSTORM is mostly used to image integral mitochondrial proteins, and there is little or no information on proteins transiently present at this compartment. The cancer-related Aurora kinase A/AURKA is a protein localized at various subcellular locations, including mitochondria.

RESULTS

We first demonstrate that dSTORM coupled to GcoPS can resolve protein proximities within individual submitochondrial compartments. Then, we show that dSTORM provides sufficient spatial resolution to visualize and quantify the most abundant pool of endogenous AURKA in the mitochondrial matrix, as previously shown for overexpressed AURKA. In addition, we uncover a smaller pool of AURKA localized at the OMM, which could have a potential functional readout. We conclude by demonstrating that aldehyde-based fixatives are more specific for the OMM pool of the kinase instead.

CONCLUSIONS

Our results indicate that dSTORM coupled to GcoPS colocalization analysis is a suitable approach to explore the compartmentalization of non-integral mitochondrial proteins as AURKA, in a qualitative and quantitative manner. This method also opens up the possibility of analyzing the proximity between AURKA and its multiple mitochondrial partners with exquisite spatial resolution, thereby allowing novel insights into the mitochondrial functions controlled by AURKA.

SIGNIFICANCE

Probing and quantifying the presence of endogenous AURKA - a cell cycle-related protein localized at mitochondria - in the different organelle subcompartments, using quantitative dSTORM super-resolution microscopy.

Neuronal ER-plasma membrane junctions organized by Kv2-VAP pairing recruit Nir proteins and affect phosphoinositide homeostasis

The association of plasma membrane (PM)-localized voltage-gated potassium (Kv2) channels with endoplasmic reticulum (ER)-localized vesicle-associated membrane protein-associated proteins VAPA and VAPB defines ER-PM junctions in mammalian brain neurons. Here, we used proteomics to identify proteins associated with Kv2/VAP-containing ER-PM junctions. We found that the VAP-interacting membrane-associated phosphatidylinositol (PtdIns) transfer proteins PYK2 N-terminal domain-interacting receptor 2 (Nir2) and Nir3 specifically associate with Kv2.1 complexes. When coexpressed with Kv2.1 and VAPA in HEK293T cells, Nir2 colocalized with cell-surface-conducting and -nonconducting Kv2.1 isoforms. This was enhanced by muscarinic-mediated PtdIns(4,5)P₂ hydrolysis, leading to dynamic recruitment of Nir2 to Kv2.1 clusters. In cultured rat hippocampal neurons, exogenously expressed Nir2 did not strongly colocalize with Kv2.1, unless exogenous VAPA was also expressed, supporting the notion that VAPA mediates the spatial association of Kv2.1 and Nir2. Immunolabeling signals of endogenous Kv2.1, Nir2, and VAP puncta were spatially correlated in cultured neurons. Fluorescence-recovery-after-photobleaching experiments revealed that Kv2.1, VAPA, and Nir2 have comparable turnover rates at ER-PM junctions, suggesting that they form complexes at these sites. Exogenous Kv2.1 expression in HEK293T cells resulted in significant differences in the kinetics of PtdIns(4,5)P₂ recovery following repetitive muscarinic stimulation, with no apparent impact on resting PtdIns(4,5)P₂ or PtdIns(4)P levels. Finally, the brains of Kv2.1-knockout mice had altered composition of PtdIns lipids, suggesting a crucial role for native Kv2.1-containing ER-PM junctions in regulating PtdIns lipid metabolism in brain neurons. These results suggest that ER-PM junctions formed by Kv2 channel-VAP pairing regulate PtdIns lipid homeostasis via VAP-associated PtdIns transfer proteins.