Summary: The pandemic coronavirus SARS‐CoV‐2 in the world has caused a large infected population suffering from COVID‐19. To curb the spreading of the virus, WHO urgently demanded an extension of screening and testing; thus, a rapid and simple diagnostic method is needed. We applied a reverse transcription‐loop‐mediated isothermal amplification (RT‐LAMP) to achieve the detection of SARS‐CoV‐2 in 30 min. We designed four sets of LAMP primers (6 primers in each set), targeting the viral RNA of SARS‐CoV‐2 in the regions of orf1ab, S gene and N gene. A colorimetric change was used to report the results, which enables the outcome of viral RNA amplification to be read by the naked eye without the need of expensive or dedicated instrument. The sensitivity can be 80 copies of viral RNA per ml in a sample. We validated the RT‐LAMP method in a hospital in China, employing 16 clinic samples with 8 positives and 8 negatives. The testing results are consistent with the conventional RT‐qPCR. In addition, we also show that one‐step process without RNA extraction is feasible to achieve RNA amplification directly from a sample. This rapid, simple and sensitive RT‐LAMP method paves a way for a large screening at public domain and hospitals, particularly regional hospitals and medical centres in rural areas.

Antimicrobial resistance (AMR) has become a global clinical problem in recent years. With the discovery of antibiotics, infections were not a deadly problem for clinicians as they used to be. However, worldwide AMR comes with the overuse/misuse of antibiotics and the spread of resistance is deteriorated by a multitude of mobile genetic elements and relevant resistant genes. This review provides an overview of the current situation, mechanism, epidemiology, detection methods and clinical treatment for antimicrobial resistant genes in clinical important bacteria including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), penicillin-resistant Streptococcus pneumoniae (PRSP), extended-spectrum β-lactamase-producing Enterobacteriaceae, acquired AmpC β-lactamase-producing Enterobacteriaceae, carbapenemase-producing Enterobacteriaceae (CPE), multidrug-resistant (MDR) Acinetobacter baumannii and Pseudomonas aeruginosa.

Rapid profiling of stress-response at single-cell resolution yet in a label-free, non-disruptive and mechanism-specific manner can lead to many new applications. We propose a single-cell-level biochemical fingerprinting approach named “ramanome”, which is the collection of Single-cell Raman Spectra (SCRS) from a number of cells randomly selected from an isogenic population at a given time and condition, to rapidly and quantitatively detect and characterize stress responses of cellular population. SCRS of Escherichia coli cells are sensitive to both exposure time (eight time points) and dosage (six doses) of ethanol, with detection time as early as 5 min and discrimination rate of either factor over 80%. Moreover, the ramanomes upon six chemical compounds from three categories, including antibiotics of ampicillin and kanamycin, alcohols of ethanol and n-butanol and heavy metals of Cu²⁺ and Cr⁶⁺, were analyzed and 31 marker Raman bands were revealed which distinguish stress-responses via cytotoxicity mechanism and variation of inter-cellular heterogeneity. Furthermore, specificity, reproducibility and mechanistic basis of ramanome were validated by tracking stress-induced dynamics of metabolites and by contrasting between cells with and without genes that convey stress resistance. Thus ramanome enables rapid prediction and mechanism-based screening of cytotoxicity and stress-response programs at single-cell resolution.

Significance: We developed a label-free and noninvasive single-cell Raman microspectroscopy (SCRM)-based platform to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). Through large-scale Raman spectral analysis, we can distinguish hiPSCs and hiPSC-derived neural cells using their intrinsic biochemical profile. We identified glycogen as a Raman biomarker for neuronal differentiation and validated the results using conventional glycogen detection assays. The parameters obtained from SCRM were processed by a novel machine learning method based on t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, enabling highly accurate and robust cell classification. The platform and the proposed biomarker should also be applicable to other cell types and can shed light on developmental biology and glycogen metabolism disorders.

The interactions between microorganisms driven by substrate metabolism and energy flow are important to shape diversity, abundance, and structure of a microbial community. Single cell technologies are useful tools for dissecting the functions of individual members and their interactions in microbial communities. Here, we developed a novel Raman stable isotope probing (Raman-SIP), which uses Raman microspectroscopy coupled with reverse and D₂O colabeling to study metabolic interactions in a two-species community consisting of Acinetobacter baylyi ADP1 and Escherichia coli DH5α-GFP. This Raman-SIP approach is able to detect carbon assimilation and general metabolic activity simultaneously. Taking advantage of Raman shift of single cell Raman spectra (SCRS) mediated by incorporation of stable-isotopic substrates, Raman-SIP with reverse labeling has been applied to detect initially ¹³C-labeled bands of ADP1 SCRS reverting back to ¹²C positions in the presence of ¹²C citrate. Raman-SIP with D₂O labeling has been employed to probe metabolic activity of single cells without the need of cell replication. Our results show that E. coli alone in minimal medium with citrate as the sole carbon source had no metabolic activity, but became metabolically active in the presence of ADP1. Mass spectrometry-based metabolite footprint analysis suggests that putrescine and phenylalanine excreted by ADP1 cells may support the metabolic activity of E. coli. This study demonstrates that Raman-SIP with reverse labeling would be a useful tool to probe metabolism of any carbon substrate, overcoming limitations when stable isotopic substrates are not readily available. It is also found that Raman-SIP with D₂O labeling is a sensitive and reliable approach to distinguish metabolically active cells but not quiescent cells. This novel approach extends the application of Raman-SIP and demonstrates its potential application as a valuable strategic approach for probing cellular metabolism, metabolic activity, and interactions in microbial communities at the single cell level.