Human neural stem cells.

'Human neural stem cells' jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research, is the first guideline for human neural stem cells (hNSCs) in China. This standard specifies the technical requirements, test methods, test regulations, instructions for use, labelling requirements, packaging requirements, storage requirements, transportation requirements and waste disposal requirements for hNSCs, which is applicable to the quality control for hNSCs. It was originally released by the China Society for Cell Biology on 30 August 2022. We hope that publication of the guideline will facilitate institutional establishment, acceptance and execution of proper protocols, and accelerate the international standardization of hNSCs for clinical development and therapeutic applications.

Human midbrain dopaminergic progenitors.

Human midbrain dopaminergic progenitors (mDAPs) are one of the most representative cell types in both basic research and clinical applications. However, there are still many challenges for the preparation and quality control of mDAPs, such as the lack of standards. Therefore, the establishment of critical quality attributes and technical specifications for mDAPs is largely needed. "Human midbrain dopaminergic progenitor" jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research, is the first guideline for human mDAPs in China. This standard specifies the technical requirements, test methods, inspection rules, instructions for usage, labelling requirements, packaging requirements, storage requirements, transportation requirements and waste disposal requirements for human mDAPs, which is applicable to the quality control for human mDAPs. It was originally released by the China Society for Cell Biology on 30 August 2022. We hope that the publication of this guideline will facilitate the institutional establishment, acceptance and execution of proper protocols, and accelerate the international standardization of human mDAPs for clinical development and therapeutic applications.

Single-molecule study reveals Hmo1, not Hho1, promotes chromatin assembly in budding yeast.

Linker histone H1 plays a crucial role in various biological processes, including nucleosome stabilization, high-order chromatin structure organization, gene expression, and epigenetic regulation in eukaryotic cells. Unlike higher eukaryotes, little about the linker histone in Saccharomyces cerevisiae is known. Hho1 and Hmo1 are two long-standing controversial histone H1 candidates in budding yeast. In this study, we directly observed at the single-molecule level that Hmo1, but not Hho1, is involved in chromatin assembly in the yeast nucleoplasmic extracts (YNPE), which can replicate the physiological condition of the yeast nucleus. The presence of Hmo1 facilitates the assembly of nucleosomes on DNA in YNPE, as revealed by single-molecule force spectroscopy. Further single-molecule analysis showed that the lysine-rich C-terminal domain (CTD) of Hmo1 is essential for the function of chromatin compaction, while the second globular domain at the C-terminus of Hho1 impairs its ability. In addition, Hmo1, but not Hho1, forms condensates with double-stranded DNA via reversible phase separation. The phosphorylation fluctuation of Hmo1 coincides with metazoan H1 during the cell cycle. Our data suggest that Hmo1, but not Hho1, possesses some functionality similar to that of linker histone in Saccharomyces cerevisiae, even though some properties of Hmo1 differ from those of a canonical linker histone H1. Our study provides clues for the linker histone H1 in budding yeast and provides insights into the evolution and diversity of histone H1 across eukaryotes. IMPORTANCE There has been a long-standing debate regarding the identity of linker histone H1 in budding yeast. To address this issue, we utilized YNPE, which accurately replicate the physiological conditions in yeast nuclei, in combination with total internal reflection fluorescence microscopy and magnetic tweezers. Our findings demonstrated that Hmo1, rather than Hho1, is responsible for chromatin assembly in budding yeast. Additionally, we found that Hmo1 shares certain characteristics with histone H1, including phase separation and phosphorylation fluctuations throughout the cell cycle. Furthermore, we discovered that the lysine-rich domain of Hho1 is buried by its second globular domain at the C-terminus, resulting in the loss of function that is similar to histone H1. Our study provides compelling evidence to suggest that Hmo1 shares linker histone H1 function in budding yeast and contributes to our understanding of the evolution of linker histone H1 across eukaryotes.

Dynamically Quantifying Intracellular Elemental Sulfur and Predicting Pertinent Gene Transcription by Raman Spectroscopy in Living Cells.

The ability to monitor changes in metabolites and corresponding gene transcription within living cells is highly desirable. However, most current assays for quantification of metabolites or for gene transcription are destructive, precluding tracking the real-time dynamics of living cells. Here, we used the intracellular elemental sulfur in a Thiophaeococcus mangrovi cell as a proof-of-concept to link the quantity of metabolites and relevant gene transcription in living cells by a nondestructive Raman approach. Raman spectroscopy was utilized to quantify intracellular elemental sulfur noninvasively, and a computational mRR (mRNA and Raman) model was developed to infer the transcription of genes relevant to elemental sulfur. The results showed a significant linear correlation between the exponentially transformed Raman spectral intensity of intracellular elemental sulfur and the mRNA levels of genes encoding sulfur globule proteins in T. mangrovi. The mRR model was verified independently in two genera of Thiocapsa and Thiorhodococcus, and the mRNA levels predicted by mRR showed high consistency with actual gene expression detected by real-time polymerase chain reaction (PCR). This approach could enable noninvasive assessment of the quantity of metabolites and link the pertinent gene expression profiles in living cells, providing useful baseline data to spectroscopically map various omics in real time.

Inactivation of porcine epidemic diarrhea virus with electron beam irradiation under cold chain conditions.

The many instances of COVID-19 outbreaks suggest that cold chains are a possible route for the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, owing to the low temperatures of cold chains, which are normally below 0 °C, there are limited options for virus inactivation. Here, high-energy electron beam (E-beam) irradiation was used to inactivate porcine epidemic diarrhea virus (PEDV) under simulated cold chain conditions. This coronavirus was used as a surrogate for SARS-CoV-2. The possible mechanism by which high-energy E-beam irradiation inactivates PEDV was also explored. An irradiation dose of 10 kGy reduced the PEDV infectious viral titer by 1.68-1.76 log10TCID 50 / 100 µ L in the cold chain environment, suggesting that greater than 98.1% of PEDV was inactivated. E-beam irradiation at 5-30 kGy damaged the viral genomic RNA with an efficiency of 46.25%-92.11%. The integrity of the viral capsid was disrupted at 20 kGy. The rapid and effective inactivation of PEDV at temperatures below freezing indicates high-energy E-beam irradiation as a promising technology for disinfecting SARS-CoV-2 in cold chain logistics to limit the transmission of COVID-19.

Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning.

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.

Structural insights into multifunctionality of human FACT complex subunit hSSRP1.

Human structure-specific recognition protein 1 (hSSRP1) is an essential component of the facilitates chromatin transcription complex, which participates in nucleosome disassembly and reassembly during gene transcription and DNA replication and repair. Many functions, including nuclear localization, histone chaperone activity, DNA binding, and interaction with cellular proteins, are attributed to hSSRP1, which contains multiple well-defined domains, including four pleckstrin homology (PH) domains and a high-mobility group domain with two flanking disordered regions. However, little is known about the mechanisms by which these domains cooperate to carry out hSSRP1's functions. Here, we report the biochemical characterization and structure of each functional domain of hSSRP1, including the N-terminal PH1, PH2, PH3/4 tandem PH, and DNA-binding high-mobility group domains. Furthermore, two casein kinase II binding sites in hSSRP1 were identified in the PH3/4 domain and in a disordered region (Gly617-Glu709) located in the C-terminus of hSSRP1. In addition, a histone H2A-H2B binding motif and a nuclear localization signal (Lys677‒Asp687) of hSSRP1 are reported for the first time. Taken together, these studies provide novel insights into the structural basis for hSSRP1 functionality.

Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens.

The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism Urechis unicinctus, including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data.

Blood identification at the single-cell level based on a combination of laser tweezers Raman spectroscopy and machine learning.

Laser tweezers Raman spectroscopy (LTRS) combines optical tweezers technology and Raman spectroscopy to obtain biomolecular compositional information from a single cell without invasion or destruction, so it can be used to "fingerprint" substances to characterize numerous types of biological cell samples. In the current study, LTRS was combined with two machine learning algorithms, principal component analysis (PCA)-linear discriminant analysis (LDA) and random forest, to achieve high-precision multi-species blood classification at the single-cell level. The accuracies of the two classification models were 96.60% and 96.84%, respectively. Meanwhile, compared with PCA-LDA and other classification algorithms, the random forest algorithm is proved to have significant advantages, which can directly explain the importance of spectral features at the molecular level.

Classification of pathogens by Raman spectroscopy combined with generative adversarial networks.

Rapid identification of marine pathogens is very important in marine ecology. Artificial intelligence combined with Raman spectroscopy is a promising choice for identifying marine pathogens due to its rapidity and efficiency. However, considering the cost of sample collection and the challenging nature of the experimental environment, only limited spectra are typically available to build a classification model, which hinders qualitative analysis. In this paper, we propose a novel method to classify marine pathogens by means of Raman spectroscopy combined with generative adversarial networks (GANs). Three marine strains, namely, Staphylococcus hominis, Vibrio alginolyticus, and Bacillus licheniformis, were cultured. Using Raman spectroscopy, we acquired 100 spectra of each strain, and we fitted them into GAN models for training. After 30,000 training iterations, the spectra generated by G were similar to the actual spectra, and D was used to test the accuracy of the spectra. Our results demonstrate that our method not only improves the accuracy of machine learning classification but also solves the problem of requiring a large amount of training data. Moreover, we have attempted to find potential identifying regions in the Raman spectra that can be used for reference in subsequent related work in this field. Therefore, this method has tremendous potential to be developed as a tool for pathogen identification.

Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification.

Raman spectroscopy is a nondestructive, label-free, highly specific approach that provides the chemical information on materials. Thus, it is suitable to be used as an effective analytical tool to characterize biological samples. Here we introduce a novel method that uses artificial intelligence to analyze biological Raman spectra and identify the microbes at a single-cell level. The combination of a framework of convolutional neural network (ConvNet) and Raman spectroscopy allows the extraction of the Raman spectral features of a single microbial cell and then categorizes cells according to their spectral features. As the proof of concept, we measured Raman spectra of 14 microbial species at a single-cell level and constructed an optimal ConvNet model using the Raman data. The average accuracy of classification by ConvNet is 95.64 ± 5.46%. Meanwhile, we introduced an occlusion-based Raman spectra feature extraction to visualize the weights of Raman features for distinguishing different species.

Kinetic analysis of DNA compaction by mycobacterial integration host factor at the single-molecule level.

Nucleoid-associated proteins (NAPs) play an important role on chromosome condensation and organization. Mycobacterial integration host factor (mIHF) is one of the few mycobacterial NAPs identified so far. mIHF has the ability to stimulate mycobacteriophage L5 integration and compact DNA into nucleoid-like or higher order filamentous structures by atomic force microscopy observation. In this study, M. smegmatis IHF (MsIHF), which possesses the sequence essential for mIHF's functions, binds 30-bp dsDNA fragments in a sequence-independent manner and displays sensitivity to ion strength in bio-layer interferometry (BLI) experiments. The DNA compaction process of MsIHF was observed at the single-molecule level using the total internal reflection fluorescence microscopy (TIRFM). MsIHF efficiently compacted λ DNA into a highly condensed structure with the concentration of 0.25 and 1.0 µM, and the packing ratios were higher than 10. Further kinetic analysis revealed MsIHF compacts DNA in a three-step mechanism, which consists of two compaction steps with different compacting rates separated by a lag step. This study would help us better understand the mechanisms of chromosomal DNA organization in mycobacteria.

Visualizing the Interaction Between the Qdot-labeled Protein and Site-specifically Modified λ DNA at the Single Molecule Level.

The fluorescence microscopy has made great contributions in dissecting the mechanisms of complex biological processes at the single molecule level. In single molecule assays for studying DNA-protein interactions, there are two important factors for consideration: the DNA substrate with enough length for easy observation and labeling a protein with a suitable fluorescent probe. 48.5 kb λ DNA is a good candidate for the DNA substrate. Quantum dots (Qdots), as a class of fluorescent probes, allow long-time observation (minutes to hours) and high-quality image acquisition. In this paper, we present a protocol to study DNA-protein interactions at the single-molecule level, which includes preparing a site-specifically modified λ DNA and labeling a target protein with streptavidin-coated Qdots. For a proof of concept, we choose ORC (origin recognition complex) in budding yeast as a protein of interest and visualize its interaction with an ARS (autonomously replicating sequence) using TIRFM. Compared with other fluorescent probes, Qdots have obvious advantages in single molecule studies due to its high stability against photobleaching, but it should be noted that this property limits its application in quantitative assays.

A method for labeling proteins with tags at the native genomic loci in budding yeast.

Fluorescent proteins and epitope tags are often used as protein fusion tags to study target proteins. One prevailing technique in the budding yeast Saccharomyces cerevisiae is to fuse these tags to a target gene at the precise chromosomal location via homologous recombination. However, several limitations hamper the application of this technique, such as the selectable markers not being reusable, tagging of only the C-terminal being possible, and a "scar" sequence being left in the genome. Here, we describe a strategy to solve these problems by tagging target genes based on a pop-in/pop-out and counter-selection system. Three fluorescent protein tag (mCherry, sfGFP, and mKikGR) and two epitope tag (HA and 3×FLAG) constructs were developed and utilized to tag HHT1, UBC13 or RAD5 at the chromosomal locus as proof-of-concept.

Rapid, label-free identification of cerebellar structures using multiphoton microscopy.

The cerebellum is the prominent laminar structure of the mammalian brain that has been implicated in various psychiatric and neurological diseases. Although clinical brain imaging techniques have provided precise anatomic images of cerebellar structures, a definitive diagnosis still requires adequate resolution to identify individual layers in cerebellar cortex, the extent of tumor, even requires the histological tissue examination during surgical procedures. In this study, multiphoton microscopy (MPM), based on second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), was perform on the rat cerebellar structures and pathology with the combination of image analysis methods. Results show that MPM can reveal the cerebellar vermis, hemispheres, medulla, and ventricle, as well as axon bundles, Purkinje cells, capillaries, and the pia mater of the cerebellum. Together with custom-developed image processing algorithms, MPM could further differentiate between the gray and white matter, as well as evaluate the Purkinje cell layer, identify the cerebellar tumor boundary, and distinguish between the tumor core and peritumor regions. Our results establish a direct visualization and rapid assessment approach for the cerebellar structures, as well as suggest the feasibility of in vivo multiphoton microendoscopes and fiberscopes as clinical tools for neuropathological diagnoses.