An Efficient GUI-Based Clustering Software for Simulation and Bayesian Cluster Analysis of Single-Molecule Localization Microscopy Data

Ligand binding of membrane proteins triggers many important cellular signaling events by the lateral aggregation of ligand-bound and other membrane proteins in the plane of the plasma membrane. This local clustering can lead to the co-enrichment of molecules that create an intracellular signal or bring sufficient amounts of activity together to shift an existing equilibrium towards the execution of a signaling event. In this way, clustering can serve as a cellular switch. The underlying uneven distribution and local enrichment of the signaling cluster’s constituting membrane proteins can be used as a functional readout. This information is obtained by combining single-molecule fluorescence microscopy with cluster algorithms that can reliably and reproducibly distinguish clusters from fluctuations in the background noise to generate quantitative data on this complex process. Cluster analysis of single-molecule fluorescence microscopy data has emerged as a proliferative field, and several algorithms and software solutions have been put forward. However, in most cases, such cluster algorithms require multiple analysis parameters to be defined by the user, which may lead to biased results. Furthermore, most cluster algorithms neglect the individual localization precision connected to every localized molecule, leading to imprecise results. Bayesian cluster analysis has been put forward to overcome these problems, but so far, it has entailed high computational cost, increasing runtime drastically. Finally, most software is challenging to use as they require advanced technical knowledge to operate. Here we combined three advanced cluster algorithms with the Bayesian approach and parallelization in a user-friendly GUI and achieved up to an order of magnitude faster processing than for previous approaches. Our work will simplify access to a well-controlled analysis of clustering data generated by SMLM and significantly accelerate data processing. The inclusion of a simulation mode aids in the design of well-controlled experimental assays.

An efficient GUI-based clustering software for simulation and Bayesian cluster analysis of single-molecule localization microscopy data

Ligand binding of membrane proteins triggers many important cellular signaling events by the lateral aggregation of ligand-bound and other membrane proteins in the plane of the plasma membrane. This local clustering can lead to the co-enrichment of molecules that create an intracellular signal or bring sufficient amounts of activity together to shift an existing equilibrium towards the execution of a signaling event. In this way, clustering can serve as a cellular switch. The underlying uneven distribution and local enrichment of the signaling cluster's constituting membrane proteins can be used as a functional readout. This information is obtained by combining single-molecule fluorescence microscopy with cluster algorithms that can reliably and reproducibly distinguish clusters from fluctuations in the background noise to generate quantitative data on this complex process. Cluster analysis of single-molecule fluorescence microscopy data has emerged as a proliferative field, and several algorithms and software solutions have been put forward. However, in most cases, such cluster algorithms require multiple analysis parameters to be defined by the user, which may lead to biased results. Furthermore, most cluster algorithms neglect the individual localization precision connected to every localized molecule, leading to imprecise results. Bayesian cluster analysis has been put forward to overcome these problems, but so far, it has entailed high computational cost, increasing runtime drastically. Finally, most software is challenging to use as they require advanced technical knowledge to operate. Here we combined three advanced cluster algorithms with the Bayesian approach and parallelization in a user-friendly GUI and achieved up to an order of magnitude faster processing than for previous approaches. Our work will simplify access to a well-controlled analysis of clustering data generated by SMLM and significantly accelerate data processing. The inclusion of a simulation mode aids in the design of well-controlled experimental assays.

BAYESIAN CLUSTERING OF INDONESIAN RICE GERMPLASM

Model-based clustering where the inference on the parameters follow the Bayesian principle has been used to cluster 467 accessions of Indonesian rice germplasm which consist of released varieties, landraces, introduction lines, improved lines and wild species. A model-based Bayesian cluster analysis of genotype data can be used to evaluate the genetic backgrounds of rice populations of interest. Such analyses can be used to infer population structure, assign individuals to sub populations, and to study hybrid populations. Thus, the goal of this research was to examine the genotype data of numerous accession of rice germplasm using the model bayesian cluster analysis. The 1536 SNP-chip design was performed for genome scanning of the accession using the high throughput genotyping platform, the data of which were used for clustering. The result indicated that the germplasm can be clustered into five cluster based on similarities on genetic profile, i.e. similarities in gene frequencies across genome among individuals. Each cluster can be identified by reference lines, i.e. the lines or varieties that their genetic profile uniquely belong to one cluster and do not have or very rare introgression from lines or varieties of other clusters. Many introgressions have been identified among lines in all clusters which indicated that most of Indonesia rice germplasm, including local and introduced varieties were the results of crosses that occurred either in naturally fixation or breeding program activities that crossed one line/varieties to the others. There is also cluster in which no reference line and almost all lines/varieties in that cluster are known to have same common specific phenotype, e.g. aromatic.

Genetic Structure of Colletotrichum gloeosporioides sensu lato Isolates Infecting Papaya Inferred by Multilocus ISSR Markers

Colletotrichum gloeosporioides sensu lato is widely distributed throughout temperate and tropical regions and causes anthracnose disease in numerous plant species. Development of effective disease management strategies is dependent on, among other factors, an understanding of pathogen genetic diversity and population stratification at the intraspecific level. For 132 isolates of C. gloeosporioides sensu lato collected from papaya in Trinidad, inter-simple-sequence repeat-polymerase chain reaction (ISSR-PCR) generated 121 polymorphic loci from five ISSR primers selected from an initial screen of 22 ISSR primers. The mean percentage of polymorphic loci was 99.18%. Bayesian cluster analysis inferred three genetic subpopulations, where group 1 consisted exclusively of isolates collected in the southern part of Trinidad whereas groups 2 and 3, although genetically distinct, were mixtures of isolates collected from both the northern and southern parts of Trinidad. Principal coordinates analysis and unweighted pair-group method with arithmetic mean phylogeny were concordant with Bayesian cluster analysis and supported subdivision into the three subpopulations. Overall, the total mean gene diversity was 0.279, the mean within-population gene diversity was 0.2161, and genetic differentiation for the Trinidad population was 0.225. Regionally, northern isolates had a lower gene diversity compared with southern isolates. Nei's gene diversity was highest for group 1 (h = 0.231), followed by group 2 (h = 0.215) and group 3 (h = 0.202). Genotypic diversity was at or near maximum for all three subpopulations after clone correction. Pairwise estimates of differentiation indicated high and significant genetic differentiation among the inferred subpopulations (Weir's θ of 0.212 to 0.325). Pairwise comparisons among subpopulations suggested restricted gene flow between groups 1 and 2 and groups 1 and 3 but not between groups 2 and 3. The null hypothesis of random mating was rejected for all three inferred subpopulations. These results suggest that pathogen biology and epidemiology as well as certain evolutionary factors may play an important role in population substructuring of C. gloeosporioides sensu lato isolates infecting papaya in Trinidad.