PACMan: A software package for automated single-cell chlorophyll fluorometry.

Microalgae, small photosynthetic unicells, are of great interest to ecology, ecotoxicology and biotechnology and there is a growing need to investigate the ability of cells to photosynthesize under variable conditions. Current strategies involve hand-operated pulse-amplitude-modulated (PAM) chlorophyll fluorimeters, which can provide detailed insights into the photophysiology of entire populations- or individual cells of microalgae but are typically limited in their throughput. To increase the throughput of a commercially available MICROSCOPY-PAM system, we present the PAM Automation Control Manager ('PACMan'), an open-source Python software package that automates image acquisition, microscopy stage control and the triggering of external hardware components. PACMan comes with a user-friendly graphical user interface and is released together with a stand-alone tool (PAMalysis) for the automated calculation of per-cell maximum quantum efficiencies (= Fv /Fm ). Using these two software packages, we successfully tracked the photophysiology of >1000 individual cells of green algae (Chlamydomonas reinhardtii) and dinoflagellates (genus Symbiodiniaceae) within custom-made microfluidic devices. Compared to the manual operation of MICROSCOPY-PAM systems, this represents a 10-fold increase in throughput. During experiments, PACMan coordinated the movement of the microscope stage and triggered the MICROSCOPY-PAM system to repeatedly capture high-quality image data across multiple positions. Finally, we analyzed single-cell Fv /Fm with the manufacturer-supplied software and PAMalysis, demonstrating a median difference <0.5% between both methods. We foresee that PACMan, and its auxiliary software package will help increase the experimental throughput in a range of microalgae studies currently relying on hand-operated MICROSCOPY-PAM technologies.

Ingrained: An Automated Framework for Fusing Atomic-Scale Image Simulations into Experiments.

To fully leverage the power of image simulation to corroborate and explain patterns and structures in atomic resolution microscopy, an initial correspondence between the simulation and experimental image must be established at the outset of further high accuracy simulations or calculations. Furthermore, if simulation is to be used in context of highly automated processes or high-throughput optimization, the process of finding this correspondence itself must be automated. In this work, "ingrained," an open-source automation framework which solves for this correspondence and fuses atomic resolution image simulations into the experimental images to which they correspond, is introduced. Herein, the overall "ingrained" workflow, focusing on its application to interface structure approximations, and the development of an experimentally rationalized forward model for scanning tunneling microscopy simulation are described.

High-Content Microscopy Analysis of Subcellular Structures: Assay Development and Application to Focal Adhesion Quantification.

High-content analysis (HCA) converts raw light microscopy images to quantitative data through the automated extraction, multiparametric analysis, and classification of the relevant information content. Combined with automated high-throughput image acquisition, HCA applied to the screening of chemicals or RNAi-reagents is termed high-content screening (HCS). Its power in quantifying cell phenotypes makes HCA applicable also to routine microscopy. However, developing effective HCA and bioinformatic analysis pipelines for acquisition of biologically meaningful data in HCS is challenging. Here, the step-by-step development of an HCA assay protocol and an HCS bioinformatics analysis pipeline are described. The protocol's power is demonstrated by application to focal adhesion (FA) detection, quantitative analysis of multiple FA features, and functional annotation of signaling pathways regulating FA size, using primary data of a published RNAi screen. The assay and the underlying strategy are aimed at researchers performing microscopy-based quantitative analysis of subcellular features, on a small scale or in large HCS experiments. © 2016 by John Wiley & Sons, Inc.