Upgraded User-Friendly Image-Activated Microfluidic Cell Sorter Using an Optimized and Fast Deep Learning Algorithm.

Image-based cell sorting is essential in biological and biomedical research. The sorted cells can be used for downstream analysis to expand our knowledge of cell-to-cell differences. We previously demonstrated a user-friendly image-activated microfluidic cell sorting technique using an optimized and fast deep learning algorithm. Real-time isolation of cells was carried out using this technique with an inverted microscope. In this study, we devised a recently upgraded sorting system. The cell sorting techniques shown on the microscope were implemented as a real system. Several new features were added to make it easier for the users to conduct the real-time sorting of cells or particles. The newly added features are as follows: (1) a high-resolution linear piezo-stage is used to obtain in-focus images of the fast-flowing cells; (2) an LED strobe light was incorporated to minimize the motion blur of fast-flowing cells; and (3) a vertical syringe pump setup was used to prevent the cell sedimentation. The sorting performance of the upgraded system was demonstrated through the real-time sorting of fluorescent polystyrene beads. The sorter achieved a 99.4% sorting purity for 15 µm and 10 µm beads with an average throughput of 22.1 events per second (eps).

Regulation of BRCA1 stability through the tandem UBX domains of isoleucyl-tRNA synthetase 1.

Aminoacyl-tRNA synthetases (ARSs) have evolved to acquire various additional domains. These domains allow ARSs to communicate with other cellular proteins in order to promote non-translational functions. Vertebrate cytoplasmic isoleucyl-tRNA synthetases (IARS1s) have an uncharacterized unique domain, UNE-I. Here, we present the crystal structure of the chicken IARS1 UNE-I complexed with glutamyl-tRNA synthetase 1 (EARS1). UNE-I consists of tandem ubiquitin regulatory X (UBX) domains that interact with a distinct hairpin loop on EARS1 and protect its neighboring proteins in the multi-synthetase complex from degradation. Phosphomimetic mutation of the two serine residues in the hairpin loop releases IARS1 from the complex. IARS1 interacts with BRCA1 in the nucleus, regulates its stability by inhibiting ubiquitylation via the UBX domains, and controls DNA repair function.

Combination of an inject-and-transfer system for serial femtosecond crystallography.

Serial femtosecond crystallography (SFX) enables the determination of room-temperature crystal structures of macromolecules with minimized radiation damage and provides time-resolved molecular dynamics by pump-probe or mix-and-inject experiments. In SFX, a variety of sample delivery methods with unique advantages have been developed and applied. The combination of existing sample delivery methods can enable a new approach to SFX data collection that combines the advantages of the individual methods. This study introduces a combined inject-and-transfer system (BITS) method for sample delivery in SFX experiments: a hybrid injection and fixed-target scanning method. BITS allows for solution samples to be reliably deposited on ultraviolet ozone (UVO)-treated polyimide films, at a minimum flow rate of 0.5 nl min-1, in both vertical and horizontal scanning modes. To utilize BITS in SFX experiments, lysozyme crystal samples were embedded in a viscous lard medium and injected at flow rates of 50-100 nl min-1 through a syringe needle onto a UVO-treated polyimide film, which was mounted on a fixed-target scan stage. The crystal samples deposited on the film were raster scanned with an X-ray free electron laser using a motion stage in both horizontal and vertical directions. Using the BITS method, the room-temperature structure of lysozyme was successfully determined at a resolution of 2.1 Å, and thus BITS could be utilized in future SFX experiments.

User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm.

Image-activated cell sorting is an essential biomedical research technique for understanding the unique characteristics of single cells. Deep learning algorithms can be used to extract hidden cell features from high-content image information to enable the discrimination of cell-to-cell differences in image-activated cell sorters. However, such systems are challenging to implement from a technical perspective due to the advanced imaging and sorting requirements and the long processing times of deep learning algorithms. Here, we introduce a user-friendly image-activated microfluidic sorting technique based on a fast deep learning model under the TensorRT framework to enable sorting decisions within 3 ms. The proposed sorter employs a significantly simplified operational procedure based on the use of a syringe connected to a piezoelectric actuator. The sorter has a 2.5 ms latency. The utility of the sorter was demonstrated through real-time sorting of fluorescent polystyrene beads and cells. The sorter achieved 98.0%, 95.1%, and 94.2% sorting purities for 15 µm and 10 µm beads, HL-60 and Jurkat cells, and HL-60 and K562 cells, respectively, with a throughput of up to 82.8 events per second (eps).

Application of a high-throughput microcrystal delivery system to serial femtosecond crystallography.

Microcrystal delivery methods are pivotal in the use of serial femtosecond crystallography (SFX) to resolve the macromolecular structures of proteins. Here, the development of a novel technique and instruments for efficiently delivering microcrystals for SFX are presented. The new method, which relies on a one-dimensional fixed-target system that includes a microcrystal container, consumes an extremely low amount of sample compared with conventional two-dimensional fixed-target techniques at ambient temperature. This novel system can deliver soluble microcrystals without highly viscous carrier media and, moreover, can be used as a microcrystal growth device for SFX. Diffraction data collection utilizing this advanced technique along with a real-time visual servo scan system has been successfully demonstrated for the structure determination of proteinase K microcrystals at 1.85 Šresolution.

Nylon mesh-based sample holder for fixed-target serial femtosecond crystallography.

Fixed-target serial femtosecond crystallography (FT-SFX) was an important advance in crystallography by dramatically reducing sample consumption, while maintaining the benefits of SFX for obtaining crystal structures at room temperature without radiation damage. Despite a number of advantages, preparation of a sample holder for the sample delivery in FT-SFX with the use of many crystals in a single mount at ambient temperature is challenging as it can be complicated and costly, and thus, development of an efficient sample holder is essential. In this study, we introduced a nylon mesh-based sample holder enclosed by a polyimide film. This sample holder can be rapidly manufactured using a commercially available nylon mesh with pores of a desired size at a low cost without challenging technology. Furthermore, this simple device is highly efficient in data acquisition. We performed FT-SFX using a nylon mesh-based sample holder and collected over 130,000 images on a single sample holder using a 30 Hz X-ray pulse for 1.2 h. We determined the crystal structures of lysozyme and glucose isomerase using the nylon mesh at 1.65 and 1.75 Å, respectively. The nylon mesh exposed to X-rays produced very low levels of background scattering at 3.75 and 4.30 Å, which are negligible for data analysis. Our method provides a simple and rapid but highly efficient way to deliver samples for FT-SFX.

Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip.

Imaging flow cytometry (IFC) is an emerging technology that acquires single-cell images at high-throughput for analysis of a cell population. Rich information that comes from high sensitivity and spatial resolution of a single-cell microscopic image is beneficial for single-cell analysis in various biological applications. In this paper, we present a fast image-processing pipeline (R-MOD: Real-time Moving Object Detector) based on deep learning for high-throughput microscopy-based label-free IFC in a microfluidic chip. The R-MOD pipeline acquires all single-cell images of cells in flow, and identifies the acquired images as a real-time process with minimum hardware that consists of a microscope and a high-speed camera. Experiments show that R-MOD has the fast and reliable accuracy (500 fps and 93.3% mAP), and is expected to be used as a powerful tool for biomedical and clinical applications.