An open-source, high-resolution, automated fluorescence microscope.

Fluorescence microscopy is a fundamental tool in the life sciences, but the availability of sophisticated equipment required to yield high-quality, quantitative data is a major bottleneck in data production in many laboratories worldwide. This problem has long been recognized and the abundancy of low-cost electronics and the simplification of fabrication through 3D-printing have led to the emergence of open-source scientific hardware as a research field. Cost effective fluorescence microscopes can be assembled from cheaply mass-produced components, but lag behind commercial solutions in image quality. On the other hand, blueprints of sophisticated microscopes such as light-sheet or super-resolution systems, custom-assembled from high quality parts, are available, but require a high level of expertise from the user. Here, we combine the UC2 microscopy toolbox with high-quality components and integrated electronics and software to assemble an automated high-resolution fluorescence microscope. Using this microscope, we demonstrate high resolution fluorescence imaging for fixed and live samples. When operated inside an incubator, long-term live-cell imaging over several days was possible. Our microscope reaches single molecule sensitivity, and we performed single particle tracking and SMLM super-resolution microscopy experiments in cells. Our setup costs a fraction of its commercially available counterparts but still provides a maximum of capabilities and image quality. We thus provide a proof of concept that high quality scientific data can be generated by lay users with a low-budget system and open-source software. Our system can be used for routine imaging in laboratories that do not have the means to acquire commercial systems and through its affordability can serve as teaching material to students.

UCsim2: two-dimensionally structured illumination microscopy using UC2.

State-of-the-art microscopy techniques enable the imaging of sub-diffraction barrier biological structures at the price of high costs or a lack of transparency. We try to reduce some of these barriers by presenting a super-resolution upgrade to our recently presented open-source optical toolbox UC2. Our new injection moulded parts allow larger builds with higher precision. The 4× lower manufacturing tolerance compared to three-dimensional printing makes assemblies more reproducible. By adding consumer-grade available open-source hardware such as digital mirror devices and laser projectors, we demonstrate a compact three-dimensional multimodal setup that combines image scanning microscopy and structured illumination microscopy. We demonstrate a gain in resolution and optical sectioning using the two different modes compared to the widefield limit by imaging Alexa Fluor ® 647- and Silicon Rhodamine-stained HeLa cells. We compare different objective lenses and by sharing the designs and manuals of our setup, we make super-resolution imaging available to everyone. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 2)'.

Super-resolution microscopy: a brief history and new avenues.

Super-resolution microscopy (SRM) is a fast-developing field that encompasses fluorescence imaging techniques with the capability to resolve objects below the classical diffraction limit of optical resolution. Acknowledged with the Nobel prize in 2014, numerous SRM methods have meanwhile evolved and are being widely applied in biomedical research, all with specific strengths and shortcomings. While some techniques are capable of nanometre-scale molecular resolution, others are geared towards volumetric three-dimensional multi-colour or fast live-cell imaging. In this editorial review, we pick on the latest trends in the field. We start with a brief historical overview of both conceptual and commercial developments. Next, we highlight important parameters for imaging successfully with a particular super-resolution modality. Finally, we discuss the importance of reproducibility and quality control and the significance of open-source tools in microscopy. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 2)'.

An Open-Source Modular Framework for Automated Pipetting and Imaging Applications.

The number of samples in biological experiments is continuously increasing, but complex protocols and human error in many cases lead to suboptimal data quality and hence difficulties in reproducing scientific findings. Laboratory automation can alleviate many of these problems by precisely reproducing machine-readable protocols. These instruments generally require high up-front investments, and due to the lack of open application programming interfaces (APIs), they are notoriously difficult for scientists to customize and control outside of the vendor-supplied software. Here, automated, high-throughput experiments are demonstrated for interdisciplinary research in life science that can be replicated on a modest budget, using open tools to ensure reproducibility by combining the tools OpenFlexure, Opentrons, ImJoy, and UC2. This automated sample preparation and imaging pipeline can easily be replicated and established in many laboratories as well as in educational contexts through easy-to-understand algorithms and easy-to-build microscopes. Additionally, the creation of feedback loops, with later pipetting or imaging steps depending on the analysis of previously acquired images, enables the realization of fully autonomous "smart" microscopy experiments. All documents and source files are publicly available to prove the concept of smart lab automation using inexpensive, open tools. It is believed this democratizes access to the power and repeatability of automated experiments.

Super-resolution structured illumination microscopy: past, present and future.

Structured illumination microscopy (SIM) has emerged as an essential technique for three-dimensional (3D) and live-cell super-resolution imaging. However, to date, there has not been a dedicated workshop or journal issue covering the various aspects of SIM, from bespoke hardware and software development and the use of commercial instruments to biological applications. This special issue aims to recap recent developments as well as outline future trends. In addition to SIM, we cover related topics such as complementary super-resolution microscopy techniques, computational imaging, visualization and image processing methods. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

A versatile and customizable low-cost 3D-printed open standard for microscopic imaging.

Modern microscopes used for biological imaging often present themselves as black boxes whose precise operating principle remains unknown, and whose optical resolution and price seem to be in inverse proportion to each other. With UC2 (You. See. Too.) we present a low-cost, 3D-printed, open-source, modular microscopy toolbox and demonstrate its versatility by realizing a complete microscope development cycle from concept to experimental phase. The self-contained incubator-enclosed brightfield microscope monitors monocyte to macrophage cell differentiation for seven days at cellular resolution level (e.g. 2 µm). Furthermore, by including very few additional components, the geometry is transferred into a 400 Euro light sheet fluorescence microscope for volumetric observations of a transgenic Zebrafish expressing green fluorescent protein (GFP). With this, we aim to establish an open standard in optics to facilitate interfacing with various complementary platforms. By making the content and comprehensive documentation publicly available, the systems presented here lend themselves to easy and straightforward replications, modifications, and extensions.

cellSTORM-Cost-effective super-resolution on a cellphone using dSTORM.

High optical resolution in microscopy usually goes along with costly hardware components, such as lenses, mechanical setups and cameras. Several studies proved that Single Molecular Localization Microscopy can be made affordable, relying on off-the-shelf optical components and industry grade CMOS cameras. Recent technological advantages have yielded consumer-grade camera devices with surprisingly good performance. The camera sensors of smartphones have benefited of this development. Combined with computing power smartphones provide a fantastic opportunity for "imaging on a budget". Here we show that a consumer cellphone is capable of optical super-resolution imaging by (direct) Stochastic Optical Reconstruction Microscopy (dSTORM), achieving optical resolution better than 80 nm. In addition to the use of standard reconstruction algorithms, we used a trained image-to-image generative adversarial network (GAN) to reconstruct video sequences under conditions where traditional algorithms provide sub-optimal localization performance directly on the smartphone. We believe that "cellSTORM" paves the way to make super-resolution microscopy not only affordable but available due to the ubiquity of cellphone cameras.

One-shot phase-recovery using a cellphone RGB camera on a Jamin-Lebedeff microscope.

Jamin-Lebedeff (JL) polarization interference microscopy is a classical method for determining the change in the optical path of transparent tissues. Whilst a differential interference contrast (DIC) microscopy interferes an image with itself shifted by half a point spread function, the shear between the object and reference image in a JL-microscope is about half the field of view. The optical path difference (OPD) between the sample and reference region (assumed to be empty) is encoded into a color by white-light interference. From a color-table, the Michel-Levy chart, the OPD can be deduced. In cytology JL-imaging can be used as a way to determine the OPD which closely corresponds to the dry mass per area of cells in a single image. Like in other interference microscopy methods (e.g. holography), we present a phase retrieval method relying on single-shot measurements only, thus allowing real-time quantitative phase measurements. This is achieved by adding several customized 3D-printed parts (e.g. rotational polarization-filter holders) and a modern cellphone with an RGB-camera to the Jamin-Lebedeff setup, thus bringing an old microscope back to life. The algorithm is calibrated using a reference image of a known phase object (e.g. optical fiber). A gradient-descent based inverse problem generates an inverse look-up-table (LUT) which is used to convert the measured RGB signal of a phase-sample into an OPD. To account for possible ambiguities in the phase-map or phase-unwrapping artifacts we introduce a total-variation based regularization. We present results from fixed and living biological samples as well as reference samples for comparison.

Using machine-learning to optimize phase contrast in a low-cost cellphone microscope.

Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light's phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. Dedicated illumination approaches, tailored to the sample under investigation help to boost the contrast. This is achieved by a programmable illumination source, which also allows to measure the phase gradient using the differential phase contrast (DPC) [1, 2] or even the quantitative phase using the derived qDPC approach [3]. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 $ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements.