Machine-learning-optimized Cas12a barcoding enables the recovery of single-cell lineages and transcriptional profiles.

The development of CRISPR-based barcoding methods creates an exciting opportunity to understand cellular phylogenies. We present a compact, tunable, high-capacity Cas12a barcoding system called dual acting inverted site array (DAISY). We combined high-throughput screening and machine learning to predict and optimize the 60-bp DAISY barcode sequences. After optimization, top-performing barcodes had ∼10-fold increased capacity relative to the best random-screened designs and performed reliably across diverse cell types. DAISY barcode arrays generated ∼12 bits of entropy and ∼66,000 unique barcodes. Thus, DAISY barcodes-at a fraction of the size of Cas9 barcodes-achieved high-capacity barcoding. We coupled DAISY barcoding with single-cell RNA-seq to recover lineages and gene expression profiles from ∼47,000 human melanoma cells. A single DAISY barcode recovered up to ∼700 lineages from one parental cell. This analysis revealed heritable single-cell gene expression and potential epigenetic modulation of memory gene transcription. Overall, Cas12a DAISY barcoding is an efficient tool for investigating cell-state dynamics.

Mitigated PM2.5 Changes by the Regional Transport During the COVID-19 Lockdown in Shanghai, China.

Intensive observations and WRF-Chem simulations are applied in this study to investigate the adverse impacts of regional transport on the PM2.5 (fine particulate matter; diameter ≤2.5 µm) changes in Shanghai during the Coronavirus Disease 2019 lockdown. As the local atmospheric oxidation capacity was observed to be generally weakened, strong regional transport carried by the frequent westerly winds is suggested to be the main driver of the unexpected pollution episodes, increasing the input of both primary and secondary aerosols. Contributing 40%-80% to the PM2.5, the transport contributed aerosols are simulated to exhibit less decreases (13.2%-21.8%) than the local particles (37.1%-64.8%) in urban Shanghai due to the lockdown, which largely results from the less decreased industrial and residential emissions in surrounding provinces. To reduce the influence of the transport, synergetic emission control, especially synergetic ammonia control, measures are proved to be effective strategies, which need to be considered in future regulations.

An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation.

Indoor autonomous navigation refers to the perception and exploration abilities of mobile agents in unknown indoor environments with the help of various sensors. It is the basic and one of the most important functions of mobile agents. In spite of the high performance of the single-sensor navigation method, multi-sensor fusion methods still potentially improve the perception and navigation abilities of mobile agents. This work summarizes the multi-sensor fusion methods for mobile agents' navigation by: (1) analyzing and comparing the advantages and disadvantages of a single sensor in the task of navigation; (2) introducing the mainstream technologies of multi-sensor fusion methods, including various combinations of sensors and several widely recognized multi-modal sensor datasets. Finally, we discuss the possible technique trends of multi-sensor fusion methods, especially its technique challenges in practical navigation environments.

Climate modulation of external forcing factors on air quality change in Eastern China: Implications for PM2.5 seasonal prediction.

Meteorological conditions significantly influence the frequency and duration of air pollution events, making the prediction of seasonal variations of PM2.5 concentration crucial for air quality control. This study analyzed the spatiotemporal variations of PM2.5 concentration anomalies over the past 39 years (1980-2018) in winter (November to January) over eastern China based on the empirical orthogonal function (EOF) method. Regression analysis is conducted on external forcing factors such as sea ice, sea temperature, and snow cover in the pre-autumn (September to October) using the time series of the first three modes. Nine key factors were selected, which further led to establishing a model for predicting winter PM2.5 concentration in eastern China using the long short-term memory deep learning algorithm (LSTM). Independent verification revealed that the predicted and observed PM2.5 concentration distributions were consistent, with the absolute value of deviation within 15 µg·m-3 between 2016 and 2018. The correlation coefficients between the predicted and observed values were between 0.42 and 0.93 over eight key cities in the past 10 years (2009-2018). The contribution rates of the nine factors to PM2.5 concentration were calculated to explore their impact on PM2.5 concentration during winter. The Arctic sea ice (ASI) was found to be the key contributor to the winter PM2.5 concentration in eastern China. The predictors can be monitored in real time; hence, the model provides a real-time predictive tool, improving the prospects of predicting seasonal PM2.5 pollution, especially in vulnerable regions such as eastern China.

PM2.5 extended-range forecast based on MJO and S2S using LightGBM.

We developed an extended-range fine particulate matter (PM2.5) prediction model in Shanghai using the light gradient-boosting machine (LightGBM) algorithm based on PM2.5 historical data, meteorological observational data, Subseasonal-to-Seasonal Prediction Project (S2S) forecasts and Madden-Julian Oscillation (MJO) monitoring data. The analysis and prediction results demonstrated that the MJO improved the predictive skill of the extended-range PM2.5 forecast. The MJO indexes, namely, real-time multivariate MJO series 1 (RMM1) and real-time multivariate MJO series 2 (RMM2), ranked the first, and seventh, respectively, in terms of the predictive contribution of all meteorological predictors. When the MJO was not introduced, the correlation coefficients for the forecasts on lead times of 11-40 days ranged from 0.27 to 0.55, and the root mean square errors (RMSEs) ranged from 23.4 to 31.8 µg/m3. After the MJO was introduced, the correlation coefficients for the 11-40 day forecast ranged from 0.31 to 0.56, among which those for the 16-40 day forecast substantially improved, and the RMSEs ranged from 23.2 to 28.7 µg/m3. When comparing the prediction scores, such as percent correct (PC), critical success index (CSI), and equitable threat score (ETS), the forecast model was more accurate when it introduced the MJO. A novel aspect of this study is to investigate the effects of the MJO mechanism on the meteorological conditions of air pollution in eastern China through advanced regression analysis. The MJO indexes RMM1 and RMM2 considerably impacted the geopotential height field of 28°-40° at 300-250 hPa 45 days in advance. When RMM1 increased and RMM2 decreased 45 days in advance, the 500 hPa geopotential height field weakened accordingly, and the bottom of the 500 hPa trough moved southward; thus cold air was more easily transported southward and the upstream air pollutants were transported to eastern China. With a weak ground pressure field and dry air at low altitudes, the westerly wind component increased, which led to the easier formation of a weather configuration favorable for the accumulation and transport of air pollution, thus resulting in an increase in PM2.5 concentration in the region. These findings can guide forecasters regarding the utility of MJO and S2S for subseasonal air pollution outlooks.

Integrative analysis of functional genomic screening and clinical data identifies a protective role for spironolactone in severe COVID-19.

We demonstrate that integrative analysis of CRISPR screening datasets enables network-based prioritization of prescription drugs modulating viral entry in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by developing a network-based approach called Rapid proXimity Guidance for Repurposing Investigational Drugs (RxGRID). We use our results to guide a propensity-score-matched, retrospective cohort study of 64,349 COVID-19 patients, showing that a top candidate drug, spironolactone, is associated with improved clinical prognosis, measured by intensive care unit (ICU) admission and mechanical ventilation rates. Finally, we show that spironolactone exerts a dose-dependent inhibitory effect on viral entry in human lung epithelial cells. Our RxGRID method presents a computational framework, implemented as an open-source software package, enabling genomics researchers to identify drugs likely to modulate a molecular phenotype of interest based on high-throughput screening data. Our results, derived from this method and supported by experimental and clinical analysis, add additional supporting evidence for a potential protective role of the potassium-sparing diuretic spironolactone in severe COVID-19.

Long sequence insertion via CRISPR/Cas gene-editing with transposase, recombinase, and integrase.

CRISPR/Cas-based gene-editing technologies have emerged as one of the most transformative tools in genome science over the past decade, providing unprecedented possibilities for both fundamental and translational research. Following the initial wave of innovations for gene knock-out, epigenetic/RNA modulation, and nickase-mediated base-editing, recent efforts have pivoted towards long-sequence gene editing- specifically, the insertion of large fragments (>1 kb) into the endogenous genome. In this review, we survey the development of these CRISPR/Cas-based sequence insertion methodologies in conjunction with the emergence of novel families of editing enzymes, such as transposases, single-stranded DNA-annealing proteins, recombinases, and integrases. Despite facing a number of challenges, this field continues to evolve rapidly and holds the potential to catalyze a new wave of revolutionary biomedical applications.

dCas9-based gene editing for cleavage-free genomic knock-in of long sequences.

Gene editing is a powerful tool for genome and cell engineering. Exemplified by CRISPR-Cas, gene editing could cause DNA damage and trigger DNA repair processes that are often error-prone. Such unwanted mutations and safety concerns can be exacerbated when altering long sequences. Here we couple microbial single-strand annealing proteins (SSAPs) with catalytically inactive dCas9 for gene editing. This cleavage-free gene editor, dCas9-SSAP, promotes the knock-in of long sequences in mammalian cells. The dCas9-SSAP editor has low on-target errors and minimal off-target effects, showing higher accuracy than canonical Cas9 methods. It is effective for inserting kilobase-scale sequences, with an efficiency of up to approximately 20% and robust performance across donor designs and cell types, including human stem cells. We show that dCas9-SSAP is less sensitive to inhibition of DNA repair enzymes than Cas9 references. We further performed truncation and aptamer engineering to minimize its size to fit into a single adeno-associated-virus vector for future application. Together, this tool opens opportunities towards safer long-sequence genome engineering.

CRISPR-Cas12a System With Synergistic Phage Recombination Proteins for Multiplex Precision Editing in Human Cells.

The development of CRISPR-based gene-editing technologies has brought an unprecedented revolution in the field of genome engineering. Cas12a, a member of the Class 2 Type V CRISPR-associated endonuclease family distinct from Cas9, has been repurposed and developed into versatile gene-editing tools with distinct PAM recognition sites and multiplexed gene targeting capability. However, with current CRISPR/Cas12a technologies, it remains a challenge to perform efficient and precise genome editing of long sequences in mammalian cells. To address this limitation, we utilized phage recombination enzymes and developed an efficient CRISPR/Cas12a tool for multiplexed precision editing in mammalian cells. Through protein engineering, we were able to recruit phage recombination proteins to Cas12a to enhance its homology-directed repair efficiencies. Our phage-recombination-assisted Cas12a system achieved up to 3-fold improvements for kilobase-scale knock-ins in human cells without compromising the specificity of the enzyme. The performance of this system compares favorably against Cas9 references, the commonly used enzyme for gene-editing tasks, with improved specificity. Additionally, we demonstrated multi-target editing with similar improved activities thanks to the RNA-processing activity of the Cas12a system. This compact, multi-target editing tool has the potential to assist in understanding multi-gene interactions. In particular, it paves the way for a gene therapy method for human diseases that complements existing tools and is suitable for polygenic disorders and diseases requiring long-sequence corrections.

Analysis of the immune landscape in virus-induced cancers using a novel integrative mechanism discovery approach.

BACKGROUND: The mechanisms of carcinogenesis from viral infections are extraordinarily complex and not well understood. Traditional methods of analyzing RNA-sequencing data may not be sufficient for unraveling complicated interactions between viruses and host cells. Using RNA and DNA-sequencing data from The Cancer Genome Atlas (TCGA), we aim to explore whether virus-induced tumors exhibit similar immune-associated (IA) dysregulations using a new algorithm we developed that focuses on the most important biological mechanisms involved in virus-induced cancers. Differential expression, survival correlation, and clinical variable correlations were used to identify the most clinically relevant IA genes dysregulated in 5 virus-induced cancers (HPV-induced head and neck squamous cell carcinoma, HPV-induced cervical cancer, EBV-induced stomach cancer, HBV-induced liver cancer, and HCV-induced liver cancer) after which a mechanistic approach was adopted to identify pathways implicated in IA gene dysregulation. RESULTS: Our results revealed that IA dysregulations vary with the cancer type and the virus type, but cytokine signaling pathways are dysregulated in all virus-induced cancers. Furthermore, we also found that important similarities exist between all 5 virus-induced cancers in dysregulated clinically relevant oncogenic signatures and IA pathways. Finally, we also discovered potential mechanisms for genomic alterations to induce IA gene dysregulations using our algorithm. CONCLUSIONS: Our study offers a new approach to mechanism identification through integrating functional annotations and large-scale sequencing data, which may be invaluable to the discovery of new immunotherapy targets for virus-induced cancers.

Computational methods reveal novel functionalities of PIWI-interacting RNAs in human papillomavirus-induced head and neck squamous cell carcinoma.

Human papillomavirus (HPV) infection is the fastest growing cause of head and neck squamous cell carcinoma (HNSCC) today, but its role in malignant transformation remains unclear. This study aimed to conduct a comprehensive investigation of PIWI-interacting RNA (piRNA) alterations and functionalities in HPV-induced HNSCC. Using 77 RNA-sequencing datasets from TCGA, we examined differential expression of piRNAs between HPV16(+) HNSCC and HPV(-) Normal samples, identifying a panel of 30 HPV-dysregulated piRNAs. We then computationally investigated the potential mechanistic significances of these transcripts in HPV-induced HNSCC, identifying our panel of piRNAs to associate with the protein PIWIL4 as well as the RTL family of retrotransposon-like genes, possibly through direct binding interactions. We also recognized several HPV-dysregulated transcripts for their correlations with well-documented mutations and copy number variations in HNSCC as well as HNSCC clinical variables, demonstrating the potential ability of our piRNAs to play important roles in large-scale modulation of HNSCC in addition to their direct, smaller-scale interactions in this malignancy. The differential expression of key piRNAs, including NONHSAT077364, NONHSAT102574, and NONHSAT128479, was verified in vitro by evaluating endogenous expression in HPV(+) cancer vs. HPV(-) normal cell lines. Overall, our novel study provides a rigorous investigation of piRNA dysregulation in HPV-related HNSCC, and lends critical insight into the idea that these small regulatory transcripts may play crucial and previously unidentified roles in tumor pathogenesis and progression.

Alcohol and hepatitis virus-dysregulated lncRNAs as potential biomarkers for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths because of frequent late detection and poor therapeutic outcomes, necessitating the need to identify effective biomarkers for early diagnosis and new therapeutic targets for effective treatment. Long noncoding RNAs (lncRNAs) have emerged as promising molecular markers for diagnosis and treatment. Through analysis of patient samples from The Cancer Genome Atlas database, we identified putative lncRNAs dysregulated in HCC and by its risk factors, hepatitis infection and alcohol consumption. We identified 184 lncRNAs dysregulated in HCC tumors versus paired normal samples, 53 lncRNAs dysregulated in alcohol-drinking patients with hepatitis B, and 5, 456 lncRNAs dysregulated in patients with hepatitis infection. A panel of these candidate lncRNAs' expressions correlated significantly with patient survival, clinical variables, and known genomic alteration in HCC. Two most significantly dysregulated lncRNAs in our computational analysis, lnc-CFP-1:1 and lnc-CD164L2-1:1, were validated in vitro to be dysregulated by alcohol. Our findings suggest that lncRNAs dysregulated by different etiologies of HCC serve as potential disease markers and can be further investigated to develop personalized prevention, diagnosis, and treatment strategies.

Smoking status regulates a novel panel of PIWI-interacting RNAs in head and neck squamous cell carcinoma.

OBJECTIVE: Smoking remains a primary etiological factor in head and neck squamous cell carcinoma (HNSCC). Given that non-coding RNAs (ncRNAs), including PIWI-interacting RNAs (piRNAs), have emerged as mediators of initiation and progression in head and neck malignancies, we undertook a global study of piRNA expression patterns in smoking-associated HNSCC. MATERIALS AND METHODS: Using RNA-sequencing data from 256 current smoker and lifelong nonsmoker samples in The Cancer Genome Atlas (TCGA), we analyzed the differential expression patterns of 27,127 piRNAs across patient cohorts stratified by tobacco use, with HPV16 status and tumor status taken into account. We correlated their expression to clinical characteristics and to smoking-induced alterations of PIWI proteins, the functional counterparts of piRNAs. Finally, we correlated our identified piRNAs and PIWI proteins to known chromosomal aberrations in HNSCC to understand their wider-ranging genomic effects. RESULTS AND CONCLUSION: Our analyses implicated a 13-member piRNA panel in smoking-related HNSCC, among which NONHSAT123636 and NONHSAT113708 associated with tumor stage, NONHSAT067200 with patient survival, and NONHSAT081250 with smoking-altered PIWIL1 protein expression. 6 piRNAs as well as PIWIL1 correlated with genomic alterations common to HNSCC, including TP53 mutation, TP53-3p co-occurrence, and 3q26, 8q24, and 11q13 amplification. Collectively, our findings provide novel insights into the etiology-specific piRNA landscape of smoking-induced HNSCC.

A comprehensive study of smoking-specific microRNA alterations in head and neck squamous cell carcinoma.

OBJECTIVE: While tobacco smoking is a well-known risk factor for head and neck squamous cell carcinoma (HNSCC), the molecular mechanisms underlying tobacco-induced HNSCC remain unclear. This study sought to comprehensively identify microRNA (miRNA) alterations and evaluate their clinical relevance in smoking-induced HNSCC pathogenesis and progression. MATERIALS AND METHODS: Using small RNA-sequencing data and clinical data from 145 HNSCC patients, we performed a series of differential expression and correlation analyses to identify a panel of tobacco-dysregulated miRNAs associated with key clinical characteristics in HNSCC. We then examined the expression patterns of these miRNAs in normal epithelial cell lines following exposure to cigarette smoke extract. RESULTS: Our analyses revealed distinct panels of miRNAs to be dysregulated with smoking status and associated with additional clinical features, including tumor stage, metastasis, anatomic site, and patient survival. The differential expression of key miRNAs, including miR-101, miR-181b, miR-486, and miR-1301, was verified in cigarette-treated epithelial cell lines, suggesting their potential roles in the early development of smoking-related HNSCCs. CONCLUSION: Specific alterations in miRNA expression may be traced to tobacco use and are associated with important HNSCC clinical characteristics. Future studies of these miRNAs may be valuable for furthering the understanding and targeted treatment of smoking-associated HNSCC.

The meteorological modulation on PM2.5 interannual oscillation during 2013 to 2015 in Shanghai, China.

Since the Action Plan for Air Pollution Prevention and Control (the Action Plan) was implemented at the end of 2013, the ambient air quality in China is significantly improved. However, PM2.5 (particles with diameter≤2.5µm) levels in some cities still exhibit clear interannual oscillations. For example, the annual mean PM2.5 levels in Shanghai decreased by 16.1% in 2014, while increased by 2.2% in 2015 according to year-on-year comparisons. To better understand the corresponding causes, the obliquely rotated T-mode principal component analysis (PCA) method and WRF-Chem model are jointly employed in this study. Results show that the west wind frequency and the accumulative wind (<1.8m/s) duration are the key indicators affecting local PM2.5 transport and dispersion significantly. Moreover, four typical synoptic patterns conductive to PM2.5 pollution are illustrated as mid-ward path cold (CM), eastward path cold (CE), L-share high (GL) and near high center (GC), in which GL is the most adverse circulation pattern. The year-on-year changes of meteorology have positive effects on PM2.5 year-on-year variations. The significant decline of PM2.5 levels in 2014 compared with those in 2013, as well as the obvious increase in 2015 compared with 2014, both well correspond to year-on-year variations of meteorological indicators. Model results present that PM2.5 interannual variations result from the changes of meteorology during 2013 to 2015, are consistent with measured oscillations. By comparing measured and modeled PM2.5 year-on-year variabilities, a greater PM2.5 decreasing at 9.4% is estimated under favorable meteorological conditions, while a less increasing at 6% under unfavorable meteorological conditions due to emission reductions, indicating the initial improvement has been achieved by the Action Plan. Otherwise, since the current Action Plan has difficulties in completely offsetting the PM2.5 rise attributed to the adverse weather, more stringent program should be drawn up for unfavorable meteorological conditions.