Spatial multi-omics at subcellular resolution via high-throughput in situ pairwise sequencing.

Technology for spatial multi-omics aids the discovery of new insights into cellular functions and disease mechanisms. Here we report the development and applicability of multi-omics in situ pairwise sequencing (MiP-seq), a method for the simultaneous detection of DNAs, RNAs, proteins and biomolecules at subcellular resolution. Compared with other in situ sequencing methods, MiP-seq enhances decoding capacity and reduces sequencing and imaging costs while maintaining the efficacy of detection of gene mutations, allele-specific expression and RNA modifications. MiP-seq can be integrated with in vivo calcium imaging and Raman imaging, which enabled us to generate a spatial multi-omics atlas of mouse brain tissues and to correlate gene expression with neuronal activity and cellular biochemical fingerprints. We also report a sequential dilution strategy for resolving optically crowded signals during in situ sequencing. High-throughput in situ pairwise sequencing may facilitate the multidimensional analysis of molecular and functional maps of tissues.

MGA-seq: robust identification of extrachromosomal DNA and genetic variants using multiple genetic abnormality sequencing.

Genomic abnormalities are strongly associated with cancer and infertility. In this study, we develop a simple and efficient method - multiple genetic abnormality sequencing (MGA-Seq) - to simultaneously detect structural variation, copy number variation, single-nucleotide polymorphism, homogeneously staining regions, and extrachromosomal DNA (ecDNA) from a single tube. MGA-Seq directly sequences proximity-ligated genomic fragments, yielding a dataset with concurrent genome three-dimensional and whole-genome sequencing information, enabling approximate localization of genomic structural variations and facilitating breakpoint identification. Additionally, by utilizing MGA-Seq, we map focal amplification and oncogene coamplification, thus facilitating the exploration of ecDNA's transcriptional regulatory function.

Deep learning for detecting visually impaired cataracts using fundus images.

Purpose: To develop a visual function-based deep learning system (DLS) using fundus images to screen for visually impaired cataracts. Materials and methods: A total of 8,395 fundus images (5,245 subjects) with corresponding visual function parameters collected from three clinical centers were used to develop and evaluate a DLS for classifying non-cataracts, mild cataracts, and visually impaired cataracts. Three deep learning algorithms (DenseNet121, Inception V3, and ResNet50) were leveraged to train models to obtain the best one for the system. The performance of the system was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results: The AUC of the best algorithm (DenseNet121) on the internal test dataset and the two external test datasets were 0.998 (95% CI, 0.996-0.999) to 0.999 (95% CI, 0.998-1.000),0.938 (95% CI, 0.924-0.951) to 0.966 (95% CI, 0.946-0.983) and 0.937 (95% CI, 0.918-0.953) to 0.977 (95% CI, 0.962-0.989), respectively. In the comparison between the system and cataract specialists, better performance was observed in the system for detecting visually impaired cataracts (p < 0.05). Conclusion: Our study shows the potential of a function-focused screening tool to identify visually impaired cataracts from fundus images, enabling timely patient referral to tertiary eye hospitals.

Decoding the spatial chromatin organization and dynamic epigenetic landscapes of macrophage cells during differentiation and immune activation.

Immunocytes dynamically reprogram their gene expression profiles during differentiation and immunoresponse. However, the underlying mechanism remains elusive. Here, we develop a single-cell Hi-C method and systematically delineate the 3D genome and dynamic epigenetic atlas of macrophages during these processes. We propose "degree of disorder" to measure genome organizational patterns inside topologically-associated domains, which is correlated with the chromatin epigenetic states, gene expression, and chromatin structure variability in individual cells. Furthermore, we identify that NF-κB initiates systematic chromatin conformation reorganization upon Mycobacterium tuberculosis infection. The integrated Hi-C, eQTL, and GWAS analysis depicts the atlas of the long-range target genes of mycobacterial disease susceptible loci. Among these, the SNP rs1873613 is located in the anchor of a dynamic chromatin loop with LRRK2, whose inhibitor AdoCbl could be an anti-tuberculosis drug candidate. Our study provides comprehensive resources for the 3D genome structure of immunocytes and sheds insights into the order of genome organization and the coordinated gene transcription during immunoresponse.

In Silico Infection Analysis (iSFA) Identified Coronavirus Infection and Potential Transmission Risk in Mammals.

Coronaviruses are a great source of threat to public health which could infect various species and cause diverse diseases. However, the epidemic's spreading among different species remains elusive. This study proposed an in silico infection analysis (iSFA) system that includes pathogen genome or transcript mining in transcriptome data of the potential host and performed a comprehensive analysis about the infection of 38 coronaviruses in wild animals, based on 2,257 transcriptome datasets from 89 mammals' lung and intestine, and revealed multiple potential coronavirus infections including porcine epidemic diarrhea virus (PEDV) infection in Equus burchellii. Then, through our transmission network analysis, potential intermediate hosts of five coronaviruses were identified. Notably, iSFA results suggested that the expression of coronavirus receptor genes tended to be downregulated after infection by another virus. Finally, binding affinity and interactive interface analysis of S1 protein and ACE2 from different species demonstrated the potential inter-species transmission barrier and cross-species transmission of SARS-CoV-2. Meanwhile, the iSFA system developed in this study could be further applied to conduct the source tracing and host prediction of other pathogen-induced diseases, thus contributing to the epidemic prevention and control.

Neuron-derived neuropeptide Y fine-tunes the splenic immune responses.

The nervous and immune systems are closely entwined to maintain the immune balance in health and disease. Here, we showed that LPS can activate suprarenal and celiac ganglia (SrG-CG) neurons and upregulate NPY expression in rats. Single-cell sequencing analysis revealed that knockdown of the NPY gene in SrG-CG altered the proliferation and activation of splenic lymphocytes. In a neuron and splenocyte coculture system and in vivo experiments, neuronal NPY in SrG-CG attenuated the splenic immune response. Notably, we demonstrated that neuronal NPF in Drosophila exerted a conservative immunomodulatory effect. Moreover, numerous SNPs in NPY and its receptors were significantly associated with human autoimmune diseases, which was further supported by the autoimmune disease patients and mouse model experiments. Together, we demonstrated that NPY is an ancient language for nervous-immune system crosstalk and might be utilized to alleviate inflammatory storms during infection and to modulate immune balance in autoimmune diseases.

Reprogramming Mycobacterium tuberculosis CRISPR System for Gene Editing and Genome-wide RNA Interference Screening.

Mycobacterium tuberculosis is the causative agent of tuberculosis (TB), which is still the leading cause of mortality from a single infectious disease worldwide. The development of novel anti-TB drugs and vaccines is severely hampered by the complicated and time-consuming genetic manipulation techniques for M. tuberculosis. Here, we harnessed an endogenous type III-A CRISPR/Cas10 system of M. tuberculosis for efficient gene editing and RNA interference (RNAi). This simple and easy method only needs to transform a single mini-CRISPR array plasmid, thus avoiding the introduction of exogenous protein and minimizing proteotoxicity. We demonstrated that M. tuberculosis genes can be efficiently and specifically knocked in/out by this system as confirmed by DNA high-throughput sequencing. This system was further applied to single- and multiple-gene RNAi. Moreover, we successfully performed genome-wide RNAi screening to identify M. tuberculosis genes regulating in vitro and intracellular growth. This system can be extensively used for exploring the functional genomics of M. tuberculosis and facilitate the development of novel anti-TB drugs and vaccines.

Improved Prediction of Regulatory Element Using Hybrid Abelian Complexity Features with DNA Sequences.

Abstract: Deciphering the code of cis-regulatory element (CRE) is one of the core issues of current biology. As an important category of CRE, enhancers play crucial roles in gene transcriptional regulations in a distant manner. Further, the disruption of an enhancer can cause abnormal transcription and, thus, trigger human diseases, which means that its accurate identification is currently of broad interest. Here, we introduce an innovative concept, i.e., abelian complexity function (ACF), which is a more complex extension of the classic subword complexity function, for a new coding of DNA sequences. After feature selection by an upper bound estimation and integration with DNA composition features, we developed an enhancer prediction model with hybrid abelian complexity features (HACF). Compared with existing methods, HACF shows consistently superior performance on three sources of enhancer datasets. We tested the generalization ability of HACF by scanning human chromosome 22 to validate previously reported super-enhancers. Meanwhile, we identified novel candidate enhancers which have supports from enhancer-related ENCODE ChIP-seq signals. In summary, HACF improves current enhancer prediction and may be beneficial for further prioritization of functional noncoding variants.

A directed learning strategy integrating multiple omic data improves genomic prediction.

Genomic prediction (GP) aims to construct a statistical model for predicting phenotypes using genome-wide markers and is a promising strategy for accelerating molecular plant breeding. However, current progress of phenotype prediction using genomic data alone has reached a bottleneck, and previous studies on transcriptomic and metabolomic predictions ignored genomic information. Here, we designed a novel strategy of GP called multilayered least absolute shrinkage and selection operator (MLLASSO) by integrating multiple omic data into a single model that iteratively learns three layers of genetic features (GFs) supervised by observed transcriptome and metabolome. Significantly, MLLASSO learns higher order information of gene interactions, which enables us to achieve a significant improvement of predictability of yield in rice from 0.1588 (GP alone) to 0.2451 (MLLASSO). In the prediction of the first two layers, some genes were found to be genetically predictable genes (GPGs) as their expressions were accurately predicted with genetic markers. Interestingly, we made three dramatic discoveries for the GPGs: (i) GPGs are good predictors for highly complex traits like yield; (ii) GPGs are mostly eQTL genes (cis or trans); and (iii) trait-related transcriptional factor families are enriched in GPGs. These findings support the notion that learned GFs not only are good predictors for traits but also have specific biological implications regarding regulation of gene expressions. To differentiate the new method from conventional GP models, we called MLLASSO a directed learning strategy supervised by intermediate omic data. This new prediction model appears to be more reliable and more robust than conventional GP models.

Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human.

DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species predictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the experimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation.

Accumulation of urinary 2-thiothiazolidine-4-carboxylic acid (TTCA) among workers occupationally exposed to carbon disulfide for 1 week.

OBJECTIVES: To investigate if carbon disulfide (CS(2)) accumulates after a 1-week exposure period, and how the work-shift duration and exposure magnitude affects this accumulation for the workers in viscose rayon industry. METHODS: Six 8-h and seven 12-h workers in the spinning department historically known to be exposed to high air CS(2) were recruited as the exposed groups. Seven workers from other non-CS(2)-exposed departments were recruited as non-exposure controls. Exposure monitoring covered a full work shift with personal breathing zone monitoring. Urine was collected pre- and post-shift every day throughout the 5 consecutive days. 2-Thiothiazolidine-4-carboxylic acid levels in the urine (U-TTCA) were determined. RESULTS: No detectable values were found for airborne (<0.6 ppm) and urinary (<35 ng/ml) monitoring for the control groups. The exposure levels for a 12-h shift (11.3+/-1.47) (AM+/-S.D.) were significantly greater than for an 8-h shift (6.3+/-0.64). The linear accumulation trend for daily U-TTCA across the workdays was only significant for the 12-h shift at pre-shift. Statistical significance was found in the regression of the ratios for pre-shift U-TTCA to airborne CS(2) levels on the preceding day to the day of the exposure at pre-shift for a 12-h shift (r=0.98, P=0.02). CONCLUSIONS: The U-TTCA accumulation for occupational exposure to CS(2) was exposure-magnitude-dependent. The linear equations derived in this study indicated that the U-TTCA increment at pre-shift for each additional daily 12-h exposure, after an adjustment for the CS(2) exposure level, was 0.02 mg/g creatinine/ppm of CS(2). The long-term exposure response under such repeated and intermittent conditions should be noteworthy.