A visible-light-excited fluorescence method for imaging protein crystals without added dyes

Application of ultraviolet, visible, and infrared light imaging in protein-based biopharmaceutical formulation characterization and development studies

Imaging is increasingly more utilized as analytical technology in biopharmaceutical formulation research, with applications ranging from subvisible particle characterization to thermal stability screening and residual moisture analysis. This review offers a comprehensive overview of analytical imaging for scientists active in biopharmaceutical formulation research and development, where it presents the unique information provided by the ultraviolet (UV), visible (Vis), and infrared (IR) sections in the electromagnetic spectrum. The main body of this review consists of an outline of UV, Vis, and IR imaging techniques for several (bio)physical properties that are commonly determined during protein-based biopharmaceutical formulation characterization and development studies. The review concludes with a future perspective of applied imaging within the field of biopharmaceutical formulation research.

Protein-crystal detection with a compact multimodal multiphoton microscope

There is an increasing demand for rapid, effective methods to identify and detect protein micro- and nano-crystal suspensions for serial diffraction data collection at X-ray free-electron lasers or high-intensity micro-focus synchrotron radiation sources. Here, we demonstrate a compact multimodal, multiphoton microscope, driven by a fiber-based ultrafast laser, enabling excitation wavelengths at 775 nm and 1300 nm for nonlinear optical imaging, which simultaneously records second-harmonic generation, third-harmonic generation and three-photon excited ultraviolet fluorescence to identify and detect protein crystals with high sensitivity. The instrument serves as a valuable and important tool supporting sample scoring and sample optimization in biomolecular crystallography, which we hope will increase the capabilities and productivity of serial diffraction data collection in the future.

Imaging Ca2+ Concentration and pH in Nanopores/Channels of Protein Crystals

Protein crystals are nanoporous materials. Despite this important characteristic, little is known about the conditions in the pores, also called channels. Here, we describe a method to study the calcium concentration and pH in the nanopores of thaumatin and lysozyme crystals. We load the crystal nanopores with fluorescent indicators and then perfuse the crystals with solutions of different calcium concentrations and pH while reading out the crystal's fluorescence intensity with confocal microscopy. By calibrating the fluorescence signal, we can determine the calcium concentration and pH in the nanopores. For the pH in thaumatin nanopores measured with the ratiometric pH sensor SNARF-1, we find a -0.7 pH shift compared to the bath pH corresponding to a fivefold higher proton concentration. This is similar to the -0.3 pH shift found in lysozyme nanopores. With single-wavelength probes, we find that the calcium concentration in thaumatin crystal nanopores is the same as in the bath, whereas it is 0.24 times lower in lysozyme nanopores. Summarizing, our experiments show that calcium concentration and pH in the nanopores of protein crystals can deviate significantly from that in the bath. In general, the described method can be applied for testing a wide range of ion or small-molecule concentrations in transparent nanoporous materials not only with ratiometric but also with single wavelength fluorescent indicators.

A low-cost method for visible fluorescence imaging

A wide variety of crystallization solutions are screened to establish conditions that promote the growth of a diffraction-quality crystal. Screening these conditions requires the assessment of many crystallization plates for the presence of crystals. Automated systems for screening and imaging are very expensive. A simple approach to imaging trace fluorescently labeled protein crystals in crystallization plates has been devised, and can be implemented at a cost as low as $50. The proteins β-lactoglobulin B, trypsin and purified concanavalin A (ConA) were trace fluorescently labeled using three different fluorescent probes: Cascade Yellow (CY), Carboxyrhodamine 6G (CR) and Pacific Blue (PB). A crystallization screening plate was set up using β-lactoglobulin B labeled with CR, trypsin labeled with CY, ConA labeled with each probe, and a mixture consisting of 50% PB-labeled ConA and 50% CR-labeled ConA. The wells of these plates were imaged using a commercially available macro-imaging lens attachment for smart devices that have a camera. Several types of macro lens attachments were tested with smartphones and tablets. Images with the highest quality were obtained with an iPhone 6S and an AUKEY Ora 10× macro lens. Depending upon the fluorescent probe employed and its Stokes shift, a light-emitting diode or a laser diode was used for excitation. An emission filter was used for the imaging of protein crystals labeled with CR and crystals with two-color fluorescence. This approach can also be used with microscopy systems commonly used to observe crystallization plates.

Distinguishing signal from autofluorescence in cryogenic correlated light and electron microscopy of mammalian cells

In cryogenic correlated light and electron microscopy (cryo-CLEM), frozen targets of interest are identified and located on EM grids by fluorescence microscopy and then imaged at higher resolution by cryo-EM. Whilst working with these methods, we discovered that a variety of mammalian cells exhibit strong punctate autofluorescence when imaged under cryogenic conditions (80 K). Autofluorescence originated from multilamellar bodies (MLBs) and secretory granules. Here we describe a method to distinguish fluorescent protein tags from these autofluorescent sources based on the narrower emission spectrum of the former. The method is first tested on mitochondria and then applied to examine the ultrastructural variability of secretory granules within insulin-secreting pancreatic beta-cell-derived INS-1E cells.

Locating and Visualizing Crystals for X-Ray Diffraction Experiments

Macromolecular crystallography has advanced from using macroscopic crystals, which might be >1 mm on a side, to crystals that are essentially invisible to the naked eye, or even under a standard laboratory microscope. As crystallography requires recognizing crystals when they are produced, and then placing them in an X-ray, electron, or neutron beam, this provides challenges, particularly in the case of advanced X-ray sources, where beams have very small cross sections and crystals may be vanishingly small. Methods for visualizing crystals are reviewed here, and examples of different types of cases are presented, including: standard crystals, crystals grown in mesophase, in situ crystallography, and crystals grown for X-ray Free Electron Laser or Micro Electron Diffraction experiments. As most techniques have limitations, it is desirable to have a range of complementary techniques available to identify and locate crystals. Ideally, a given technique should not cause sample damage, but sometimes it is necessary to use techniques where damage can only be minimized. For extreme circumstances, the act of probing location may be coincident with collecting X-ray diffraction data. Future challenges and directions are also discussed.

Dynamic X-ray diffraction sampling for protein crystal positioning

A sparse supervised learning approach for dynamic sampling (SLADS) is described for dose reduction in diffraction-based protein crystal positioning. Crystal centering is typically a prerequisite for macromolecular diffraction at synchrotron facilities, with X-ray diffraction mapping growing in popularity as a mechanism for localization. In X-ray raster scanning, diffraction is used to identify the crystal positions based on the detection of Bragg-like peaks in the scattering patterns; however, this additional X-ray exposure may result in detectable damage to the crystal prior to data collection. Dynamic sampling, in which preceding measurements inform the next most information-rich location to probe for image reconstruction, significantly reduced the X-ray dose experienced by protein crystals during positioning by diffraction raster scanning. The SLADS algorithm implemented herein is designed for single-pixel measurements and can select a new location to measure. In each step of SLADS, the algorithm selects the pixel, which, when measured, maximizes the expected reduction in distortion given previous measurements. Ground-truth diffraction data were obtained for a 5 µm-diameter beam and SLADS reconstructed the image sampling 31% of the total volume and only 9% of the interior of the crystal greatly reducing the X-ray dosage on the crystal. Using in situ two-photon-excited fluorescence microscopy measurements as a surrogate for diffraction imaging with a 1 µm-diameter beam, the SLADS algorithm enabled image reconstruction from a 7% sampling of the total volume and 12% sampling of the interior of the crystal. When implemented into the beamline at Argonne National Laboratory, without ground-truth images, an acceptable reconstruction was obtained with 3% of the image sampled and approximately 5% of the crystal. The incorporation of SLADS into X-ray diffraction acquisitions has the potential to significantly minimize the impact of X-ray exposure on the crystal by limiting the dose and area exposed for image reconstruction and crystal positioning using data collection hardware present in most macromolecular crystallography end-stations.