After sequencing: quantitative analysis of protein localization

ON IDENTIFYING INFORMATION FROM IMAGE-BASED SPATIAL POLARITY PHENOTYPES IN NEUTROPHILS

Cell polarity is involved in many biological functions such as development, wound healing and immune responses. In human neutrophils, polarization is characterized by the translocation of distinct sets of signaling molecules to opposite ends of the cell and the rapid rearrangement of cytoskeleton to initiate migration. While many image-based studies have described cellular morphology and the intensity level of polarity signaling molecules, systematic characterization of the spatial distribution of polarity signaling molecules has been lacking. Here we designed a collection of analytical features to quantify spatial phenotypes of polarity molecules. We compared our features to commonly used polarity readouts and found that they captured additional aspects of the polarization dynamics that were not contained in the existing features. Our work provides a starting point to identify informative features for the study of neutrophil polarization.

An approach for extensibly profiling the molecular states of cellular subpopulations

Microscopy often reveals the existence of phenotypically distinct cellular subpopulations. However, additional characterization of observed subpopulations can be limited by the number of biomolecular markers that can be simultaneously monitored. Here we present a computational approach for extensibly profiling cellular subpopulations by freeing one or more imaging channels to monitor additional probes. In our approach, we trained classifiers to re-identify subpopulations accurately based on an enhanced collection of phenotypic features extracted from only a subset of the original markers. Then we constructed subpopulation profiles step-wise from replicate experiments, in which cells were labeled with different but overlapping marker sets. We applied our approach to identify molecular differences among subpopulations and to identify functional groupings of markers, in populations of differentiating mouse preadipocytes, polarizing human neutrophil-like cells and dividing human cancer cells.

Image-based multivariate profiling of drug responses from single cells

Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on approximately 300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10-15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.

A live cell, image-based approach to understanding the enzymology and pharmacology of 2-bromopalmitate and palmitoylation

The addition of a lipid moiety to a protein increases its hydrophobicity and subsequently its attraction to lipophilic environments like membranes. Indeed most lipid-modified proteins are localized to membranes where they associate with multiprotein signaling complexes. Acylation and prenylation are the two common categories of lipidation. The enzymology and pharmacology of prenylation are well understood but relatively very little is known about palmitoylation, the most common form of acylation. One distinguishing characteristic of palmitoylation is that it is a dynamic modification. To understand more about how palmitoylation is regulated, we fused palmitoylation substrates to fluorescent proteins and reported their subcellular distribution and trafficking. We used automated high-throughput fluorescence microscopy and a specialized computer algorithm to image and measure the fraction of palmitoylation reporter on the plasma membrane versus the cytoplasm. Using this system we determined the residence half-life of palmitate on the dipalmitoyl substrate peptide from GAP43 as well as the EC(50) for 2-bromopalmitate, a common inhibitor of palmitoylation.

Automated analysis of fluorescence microscopy images to identify protein-protein interactions

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction. Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.