We have developed a new mass spectrometry (MS) technology, the Single-probe MS, capable of real-time, in situ metabolomic analysis of individual living cells. The Single-probe is a miniaturized multifunctional sampling and ionization device that is directly coupled to the mass spectrometer. With a sampling tip smaller than individual eukaryotic cells (<10 μm), the Single-probe can be inserted into single cells to sample the intracellular compounds for real-time MS analysis. We have used the Single-probe to detect several cellular metabolites and the anticancer small molecules paclitaxel, doxorubicin, and OSW-1 in individual cervical cancer cells (HeLa). Single cell mass spectrometry (SCMS) is an emerging scientific technology that could reshape the analytical science of many research disciplines, and the Single-probe MS technology is a novel method for SCMS that, through its accessible fabrication protocols, can be broadly applied to different research areas.

Ginseng, which contains ginsenosides as bioactive compounds, has been regarded as an important traditional medicine for several millennia. However, the genetic background of ginseng remains poorly understood, partly because of the plant's large and complex genome composition. We report the entire genome sequence of Panax ginseng using next-generation sequencing. The 3.5-Gb nucleotide sequence contains more than 60% repeats and encodes 42 006 predicted genes. Twenty-two transcriptome datasets and mass spectrometry images of ginseng roots were adopted to precisely quantify the functional genes. Thirty-one genes were identified to be involved in the mevalonic acid pathway. Eight of these genes were annotated as 3-hydroxy-3-methylglutaryl-CoA reductases, which displayed diverse structures and expression characteristics. A total of 225 UDP-glycosyltransferases (UGTs) were identified, and these UGTs accounted for one of the largest gene families of ginseng. Tandem repeats contributed to the duplication and divergence of UGTs. Molecular modeling of UGTs in the 71st, 74th, and 94th families revealed a regiospecific conserved motif located at the N-terminus. Molecular docking predicted that this motif captures ginsenoside precursors. The ginseng genome represents a valuable resource for understanding and improving the breeding, cultivation, and synthesis biology of this key herb.

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Abstract. Ambient mass spectrometry imaging (MSI) is an emerging field with great potential for the detailed spatial analysis of biological samples with minimal pretreatment. We have developed a miniaturized sampling and ionization device, the Singleprobe, which uses in-situ surface micro-extraction to achieve high detection sensitivity and spatial resolution during MSI experiments. The Single-probe was coupled to a Thermo LTQ Orbitrap XL mass spectrometer and was able to create high spatial and high mass resolution MS images at 8±2 and 8.5 μm on flat polycarbonate microscope slides and mouse kidney sections, respectively, which are among the highest resolutions available for ambient MSI techniques. Our proof-of-principle experiments indicate that the Single-probe MSI technique has the potential to obtain ambient MS images with very high spatial resolutions with minimal sample preparation, which opens the possibility for subcellular ambient tissue MSI to be performed in the future.

Speaker extraction aims to mimic humans' selective auditory attention by extracting a target speaker's voice from a multi-talker environment. It is common to perform the extraction in frequency-domain, and reconstruct the time-domain signal from the extracted magnitude and estimated phase spectra. However, such an approach is adversely affected by the inherent difficulty of phase estimation. Inspired by Conv-TasNet, we propose a time-domain speaker extraction network (SpEx) that converts the mixture speech into multi-scale embedding coefficients instead of decomposing the speech signal into magnitude and phase spectra. In this way, we avoid phase estimation. The SpEx network consists of four network components, namely speaker encoder, speech encoder, speaker extractor, and speech decoder. Specifically, the speech encoder converts the mixture speech into multi-scale embedding coefficients, the speaker encoder learns to represent the target speaker with a speaker embedding. The speaker extractor takes the multi-scale embedding coefficients and target speaker embedding as input and estimates a receptive mask. Finally, the speech decoder reconstructs the target speaker's speech from the masked embedding coefficients. We also propose a multi-task learning framework and a multi-scale embedding implementation. Experimental results show that the proposed SpEx achieves 37.3%, 37.7% and 15.0% relative improvements over the best baseline in terms of signal-to-distortion ratio (SDR), scale-invariant SDR (SI-SDR), and perceptual evaluation of speech quality (PESQ) under an open evaluation condition.

Oxidative stress is a well-established event in the pathology of several neurobiological diseases. Sirt3 is a nicotinamide adenine nucleotide (NAD+)-dependent protein deacetylase that regulates mitochondrial function and metabolism in response to caloric restriction and stress. This study aims to investigate the role of Sirt3 in H2O2 induced oxidative neuronal injury in primary cultured rat cortical neurons. We found that H2O2 treatment significantly increased the expression of Sirt3 in a time-dependent manner at both mRNA and protein levels. Knockdown of Sirt3 with a specific small interfering RNA (siRNA) exacerbated H2O2-induced neuronal injury, whereas overexpression of Sirt3 by lentivirus transfection inhibited H2O2-induced neuronal damage reduced the generation of reactive oxygen species (ROS), and increased the activities of endogenous antioxidant enzymes. In addition, the intra-mitochondrial Ca2+ overload, but not cytosolic Ca2+ increase after H2O2 treatment, was strongly attenuated after Sirt3 overexpression. Overexpression of Sirt3 also increased the content of mitochondrial DNA (mtDNA) and the expression of mitochondrial biogenesis related transcription factors. All these results suggest that Sirt3 acts as a prosurvival factor playing an essential role to protect cortical neurons under H2O2 induced oxidative stress, possibly through regulating mitochondrial Ca2+ homeostasis and mitochondrial biogenesis.

Utterance level permutation invariant training (uPIT) technique is a state-of-the-art deep learning architecture for speaker independent multi-talker separation. uPIT solves the label ambiguity problem by minimizing the mean square error (MSE) over all permutations between outputs and targets. However, uPIT may be sub-optimal at segmental level because the optimization is not calculated over the individual frames. In this paper, we propose a constrained uPIT (cu-PIT) to solve this problem by computing a weighted MSE loss using dynamic information (i.e., delta and acceleration). The weighted loss ensures the temporal continuity of output frames with the same speaker. Inspired by the heuristics (i.e., vocal tract continuity) in computational auditory scene analysis, we then extend the model by adding a Grid LSTM layer, that we name it as cuPIT-Grid LSTM, to automatically learn both temporal and spectral patterns over the input magnitude spectrum simultaneously. The experimental results show 9.6% and 8.5% relative improvements on WSJ0-2mix dataset under both closed and open conditions comparing with the uPIT baseline.

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