Insight into the function and regulation of biological molecules can often be obtained by determining which cell structures and other molecules they localize with (i.e. colocalization). Here we describe an open source plugin for ImageJ called EzColocalization to visualize and measure colocalization in microscopy images. EzColocalization is designed to be easy to use and customize for researchers with minimal experience in quantitative microscopy and computer programming. Features of EzColocalization include: (i) tools to select individual cells and organisms from images; (ii) filters to select specific types of cells and organisms based on physical parameters and signal intensity; (iii) heat maps and scatterplots to visualize the localization patterns of reporters; (iv) multiple metrics to measure colocalization for two or three reporters; (v) metric matrices to systematically measure colocalization at multiple combinations of signal intensity thresholds; and (vi) data tables that provide detailed information on each cell in a sample. These features make EzColocalization well-suited for experiments with low reporter signal, complex patterns of localization, and heterogeneous populations of cells and organisms.

Meiotic recombination is highly regulated to ensure precise segregation of homologous chromosomes. Evidence from diverse organisms indicates that the synaptonemal complex (SC), which assembles between paired chromosomes, plays essential roles in crossover formation and patterning. Several additional "pro-crossover" proteins are also required for recombination intermediates to become crossovers. These typically form multiple foci or recombination nodules along SCs, and later accumulate at fewer, widely spaced sites. Here we report that in C. elegans CDK-2 is required to stabilize all crossover intermediates and stabilizes interactions among pro-crossover factors by phosphorylating MSH-5. Additionally, we show that the conserved RING domain proteins ZHP-3/4 diffuse along the SC and remain dynamic following their accumulation at recombination sites. Based on these and previous findings we propose a model in which recombination nodules arise through spatially restricted biomolecular condensation and then undergo a regulated coarsening process, resulting in crossover interference.

Bacterial small RNAs (sRNAs) regulate protein production by binding to mRNAs and altering their translation and degradation. sRNAs are smaller than most mRNAs but larger than many proteins. Therefore it is uncertain whether sRNAs can enter the nucleoid to target nascent mRNAs. Here, we investigate the intracellular localization of sRNAs transcribed from plasmids in Escherichia coli using RNA fluorescent in-situ hybridization. We found that sRNAs (GlmZ, OxyS, RyhB and SgrS) have equal preference for the nucleoid and cytoplasm, and no preferential localization at the cell membrane. We show using the gfp mRNA (encoding green fluorescent protein) that non-sRNAs can be engineered to have different proportions of nucleoid and cytoplasmic localization by altering their length and/or translation. The same localization as sRNAs was achieved by decreasing gfp mRNA length and translation, which suggests that sRNAs and other RNAs may enter the densely packed DNA of the nucleoid if they are sufficiently small. We also found that the Hfq protein, which binds sRNAs, minimally affects sRNA localization. Important implications of our findings for engineering synthetic circuits are: (i) sRNAs can potentially bind nascent mRNAs in the nucleoid, and (ii) localization patterns and distribution volumes of sRNAs can differ from some larger RNAs.

Quantifying the localization of molecules with respect to other molecules, cell structures and intracellular regions is essential to understanding their regulation and actions. However, measuring localization from microscopy images is often difficult with existing metrics. Here, we evaluate a metric for quantifying localization termed the threshold overlap score (TOS), and show it is simple to calculate, easy to interpret, able to be used to systematically characterize localization patterns, and generally applicable. TOS is calculated by: (i) measuring the overlap of pixels that are above the intensity thresholds for two signals; (ii) determining whether the overlap is more, less, or the same as expected by chance, i.e. colocalization, anti-colocalization, or non-colocalization; and (iii) rescaling to allow comparison at different thresholds. The above is repeated at multiple threshold combinations to generate a TOS matrix to systematically characterize the relationship between localization and signal intensities. TOS matrices were used to identify and distinguish localization patterns of different proteins in various simulations, cell types and organisms with greater specificity and sensitivity than common metrics. For all the above reasons, TOS is an excellent first line metric, particularly for cells with mixed localization patterns.

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Meiotic chromosome segregation relies on synapsis and crossover recombination between homologous chromosomes. These processes require multiple steps that are coordinated by the meiotic cell cycle and monitored by surveillance mechanisms. In diverse species, failures in chromosome synapsis can trigger a cell cycle delay and/or lead to apoptosis. How this key step in 'homolog engagement' is sensed and transduced by meiotic cells is unknown. Here we report that in C. elegans, recruitment of the Polo-like kinase PLK-2 to the synaptonemal complex triggers phosphorylation and inactivation of CHK-2, an early meiotic kinase required for pairing, synapsis, and double-strand break induction. Inactivation of CHK-2 terminates double-strand break formation and enables crossover designation and cell cycle progression. These findings illuminate how meiotic cells ensure crossover formation and accurate chromosome segregation.

Molecular subtyping studies have allowed the allocation of cancer into groups based on similar molecular, morphological, and clinical characteristics. Such studies are critical to help researchers identify actionable targets for drug design and biomarkers to predict therapeutic response. Based on multi-omics data, various approaches have been used to identify and analyze tumor subtypes and their correlation with tumor immunity and immunotherapy success. The NanoString GeoMx Digital Spatial Profiler is one approach that combines morphological context with spatial transcriptomics on a single tissue specimen. A critical step in this approach involves staining with morphology markers to identify relevant regions of interest (ROIs) for analysis. Yet, widespread adoption of GeoMx DSP has revealed a significant limitation, namely that researchers base transcriptional analysis of thousands of RNA targets on the spatial information provided by only a few morphology markers. Recent efforts have greatly expanded the availability of morphology markers to facilitate cell type-specific analyses. To evaluate the ability of this technology to selectively enrich specific cell types, we developed custom morphology markers to stain non-small-cell lung cancer (NSCLC) tissue specimens of various subtypes. Focusing on squamous cell carcinoma and adenocarcinoma, we stained with P40 and TTF-1 and transcriptionally profiled cell type-specific ROIs with the Cancer Transcriptome Atlas panel. Differential gene expression analysis of the whole transcriptome was performed using GeoMx DSP Analysis Suite software. The data reveal differences in gene expression in several key cancer pathways between tumor subtypes are correlated with the presence or absence of specific cell types. The data support the use of custom morphology markers for cell type stratification in tumor subtypes, providing more meaningful gene expression analysis. Ongoing work continues to explore the utility of this technology for cell type-specific gene expression analysis within different tumor subtypes. Citation Format: Jessica Runyon, Vijay Baichwal, Weston Stauffer, Christian Nievera. Identifying and analyzing tumor subtypes using custom morphology markers for NanoString® GeoMx® Digital Spatial Profiler. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4634.

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