A single-cell atlas enables mapping of homeostatic cellular shifts in the adult human breast.

Here we use single-cell RNA sequencing to compile a human breast cell atlas assembled from 55 donors that had undergone reduction mammoplasties or risk reduction mastectomies. From more than 800,000 cells we identified 41 cell subclusters across the epithelial, immune and stromal compartments. The contribution of these different clusters varied according to the natural history of the tissue. Age, parity and germline mutations, known to modulate the risk of developing breast cancer, affected the homeostatic cellular state of the breast in different ways. We found that immune cells from BRCA1 or BRCA2 carriers had a distinct gene expression signature indicative of potential immune exhaustion, which was validated by immunohistochemistry. This suggests that immune-escape mechanisms could manifest in non-cancerous tissues very early during tumor initiation. This atlas is a rich resource that can be used to inform novel approaches for early detection and prevention of breast cancer.

From Wigner hyperbolic rotation to fractional squeezing transformation.

Based on the usual Wigner-Weyl transformation theory we find that the Wigner hyperbolic rotation in phase space will map onto fractional squeezing operator in Hilbert space. The merit of Weyl ordering and the coherent state representation of Fresnel operator is used in our derivation.

Anionic Templates Drive Conversion between a ZnII9L6 Tricapped Trigonal Prism and ZnII6L4 Pseudo-Octahedra.

This work introduces the use of 8-aminoquinoline subcomponents to generate complex three-dimensional structures. Together with a tris(formylpyridine), 8-aminoquinoline condensed around ZnII templates to produce a tris(tridentate) ligand. This ligand is incorporated into either a tricapped trigonal prismatic ZnII9L6 structure or a pair of pseudo-octahedral ZnII6L4 diastereomers, with S4 and D2 symmetries. Introduction of a methyl group onto the aminoquinoline modulated the coordination sphere of ZnII, which favored the ZnII9L6 structure and disfavored the ZnII6L4 assembly. The tricapped trigonal prismatic ZnII9L6 architecture converted into a single ZnII6L4 cage diastereomer following the addition of a dianionic 4,4'-dinitrostilbene-2,2'-disulfonate guest. Four of these guests clustered tightly at the four windows of the ZnII6L4 cage, held in place through electrostatic interactions and hydrogen bonding, stabilize a single diastereomeric configuration with S4 symmetry.

Insights into Stereoselectivity Switch in Michael Addition-Initiated Tandem Mannich Cyclizations and Their Extension from Enamines to Vinyl Ethers.

Both cis- and trans- tetracyclic spiroindolines are the core of many important biologically active indole alkaloids, but the divergent synthesis of these important motifs is largely hampered by the limited stereoselectivity control. A facile stereoinversion protocol is reported here in Michael addition-initiated tandem Mannich cyclizations for constructing tetracyclic spiroindolines, providing an easy access to two diastereoisomeric cores of monoterpene indole alkaloids with high selectivity. The mechanistic studies including in situ NMR experiments, control experiments, and DFT calculations reveal that the reaction undergoes a unique retro-Mannich/re-Mannich rearrangement including a C-C bond cleavage that is very rare for a saturated six-membered carbocycle. Insights into the stereoinversion process have been uncovered, and the major effects were determined to be the electronic properties of N-protecting groups of the indole with the aid of Lewis acid catalysts. By understanding these insights, the stereoselectivity switching strategy is also smoothly applied from enamine substrates to vinyl ether substrates, which are enriched greatly for the divergent synthesis and stereocontrol of monoterpene indole alkaloids. The current reaction also proves to be very practical and was successfully applied to the gram-scale total synthesis of strychnine and deethylibophyllidine in short routes.

NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks.

Discovering DNA regulatory sequence motifs and their relative positions is vital to understanding the mechanisms of gene expression regulation. Although deep convolutional neural networks (CNNs) have achieved great success in predicting cis-regulatory elements, the discovery of motifs and their combinatorial patterns from these CNN models has remained difficult. We show that the main difficulty is due to the problem of multifaceted neurons which respond to multiple types of sequence patterns. Since existing interpretation methods were mainly designed to visualize the class of sequences that can activate the neuron, the resulting visualization will correspond to a mixture of patterns. Such a mixture is usually difficult to interpret without resolving the mixed patterns. We propose the NeuronMotif algorithm to interpret such neurons. Given any convolutional neuron (CN) in the network, NeuronMotif first generates a large sample of sequences capable of activating the CN, which typically consists of a mixture of patterns. Then, the sequences are "demixed" in a layer-wise manner by backward clustering of the feature maps of the involved convolutional layers. NeuronMotif can output the sequence motifs, and the syntax rules governing their combinations are depicted by position weight matrices organized in tree structures. Compared to existing methods, the motifs found by NeuronMotif have more matches to known motifs in the JASPAR database. The higher-order patterns uncovered for deep CNs are supported by the literature and ATAC-seq footprinting. Overall, NeuronMotif enables the deciphering of cis-regulatory codes from deep CNs and enhances the utility of CNN in genome interpretation.

hECA: The cell-centric assembly of a cell atlas.

The accumulation of massive single-cell omics data provides growing resources for building biomolecular atlases of all cells of human organs or the whole body. The true assembly of a cell atlas should be cell-centric rather than file-centric. We developed a unified informatics framework for seamless cell-centric data assembly and built the human Ensemble Cell Atlas (hECA) from scattered data. hECA v1.0 assembled 1,093,299 labeled human cells from 116 published datasets, covering 38 organs and 11 systems. We invented three new methods of atlas applications based on the cell-centric assembly: "in data" cell sorting for targeted data retrieval with customizable logic expressions, "quantitative portraiture" for multi-view representations of biological entities, and customizable reference creation for generating references for automatic annotations. Case studies on agile construction of user-defined sub-atlases and "in data" investigation of CAR-T off-targets in multiple organs showed the great potential enabled by the cell-centric ensemble atlas.

Toward a unified information framework for cell atlas assembly.

This perspective discusses the need and directions for the development of a unified information framework to enable the assembly of cell atlases and a revolution in medical research on the virtual body of assembled cell systems.

DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.

Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcription factors (TFs) and regulatory elements to achieve interpretable annotations of chromatin accessibility across diverse cellular contexts. To solve this problem, we propose DeepCAGE, a deep learning framework that integrates sequence information and binding statuses of TFs, for the accurate prediction of chromatin accessible regions at a genome-wide scale in a variety of cell types. DeepCAGE takes advantage of a densely connected deep convolutional neural network architecture to automatically learn sequence signatures of known chromatin accessible regions and then incorporates such features with expression levels and binding activities of human core TFs to predict novel chromatin accessible regions. In a series of systematic comparisons with existing methods, DeepCAGE exhibits superior performance in not only the classification but also the regression of chromatin accessibility signals. In a detailed analysis of TF activities, DeepCAGE successfully extracts novel binding motifs and measures the contribution of a TF to the regulation with respect to a specific locus in a certain cell type. When applied to whole-genome sequencing data analysis, our method successfully prioritizes putative deleterious variants underlying a human complex trait and thus provides insights into the understanding of disease-associated genetic variants. DeepCAGE can be downloaded from https://github.com/kimmo1019/DeepCAGE.

HGC: fast hierarchical clustering for large-scale single-cell data.

SUMMARY: Clustering is a key step in revealing heterogeneities in single-cell data. Most existing single-cell clustering methods output a fixed number of clusters without the hierarchical information. Classical hierarchical clustering (HC) provides dendrograms of cells, but cannot scale to large datasets due to high computational complexity. We present HGC, a fast Hierarchical Graph-based Clustering tool to address both problems. It combines the advantages of graph-based clustering and HC. On the shared nearest-neighbor graph of cells, HGC constructs the hierarchical tree with linear time complexity. Experiments showed that HGC enables multiresolution exploration of the biological hierarchy underlying the data, achieves state-of-the-art accuracy on benchmark data and can scale to large datasets. AVAILABILITY AND IMPLEMENTATION: The R package of HGC is available at https://bioconductor.org/packages/HGC/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SOMDE: a scalable method for identifying spatially variable genes with self-organizing map.

MOTIVATION: Recent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue microenvironments. A fundamental task in spatial gene expression analysis is to identify genes with spatially variable expression patterns, or spatially variable genes (SVgenes). Several computational methods have been developed for this task. Their high computational complexity limited their scalability to the latest and future large-scale spatial expression data. RESULTS: We present SOMDE, an efficient method for identifying SVgenes in large-scale spatial expression data. SOMDE uses self-organizing map to cluster neighboring cells into nodes, and then uses a Gaussian process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5-50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in ∼5 min in large datasets of more than 20 000 sequencing sites. SOMDE is available as a python package on PyPI at https://pypi.org/project/somde free for academic use. AVAILABILITY AND IMPLEMENTATION: SOMDE is available for download from PyPI, and the source code is openly available from the Github repository https://github.com/XuegongLab/somde. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Experiment on Ab Initio Discovery of Biological Knowledge from scRNA-Seq Data Using Machine Learning.

Expectations of machine learning (ML) are high for discovering new patterns in high-throughput biological data, but most such practices are accustomed to relying on existing knowledge conditions to design experiments. Investigations of the power and limitation of ML in revealing complex patterns from data without the guide of existing knowledge have been lacking. In this study, we conducted systematic experiments on such ab initio knowledge discovery with ML methods on single-cell RNA-sequencing data of early embryonic development. Results showed that a strategy combining unsupervised and supervised ML can reveal major cell lineages with minimum involvement of prior knowledge or manual intervention, and the ab initio mining enabled a new discovery of human early embryonic cell differentiation. The study illustrated the feasibility, significance, and limitation of ab initio ML knowledge discovery on complex biological problems.

VPAC: Variational projection for accurate clustering of single-cell transcriptomic data.

BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) technologies have advanced rapidly in recent years and enabled the quantitative characterization at a microscopic resolution. With the exponential growth of the number of cells profiled in individual scRNA-seq experiments, the demand for identifying putative cell types from the data has become a great challenge that appeals for novel computational methods. Although a variety of algorithms have recently been proposed for single-cell clustering, such limitations as low accuracy, inferior robustness, and inadequate stability greatly impede the scope of applications of these methods. RESULTS: We propose a novel model-based algorithm, named VPAC, for accurate clustering of single-cell transcriptomic data through variational projection, which assumes that single-cell samples follow a Gaussian mixture distribution in a latent space. Through comprehensive validation experiments, we demonstrate that VPAC can not only be applied to datasets of discrete counts and normalized continuous data, but also scale up well to various data dimensionality, different dataset size and different data sparsity. We further illustrate the ability of VPAC to detect genes with strong unique signatures of a specific cell type, which may shed light on the studies in system biology. We have released a user-friendly python package of VPAC in Github ( https://github.com/ShengquanChen/VPAC ). Users can directly import our VPAC class and conduct clustering without tedious installation of dependency packages. CONCLUSIONS: VPAC enables highly accurate clustering of single-cell transcriptomic data via a statistical model. We expect to see wide applications of our method to not only transcriptome studies for fully understanding the cell identity and functionality, but also the clustering of more general data.

Estimating the total genome length of a metagenomic sample using k-mers.

BACKGROUND: Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage. RESULTS: As an initial step toward understanding the complete composition of a metagenomic sample, we studied the problem of estimating the total length of all distinct component genomes in a metagenomic sample. We showed that this problem can be solved by estimating the total number of distinct k-mers in all the metagenomic sequencing data. We proposed a method for this estimation based on the sequencing coverage distribution of observed k-mers, and introduced a k-mer redundancy index (KRI) to fill in the gap between the count of distinct k-mers and the total genome length. We showed the effectiveness of the proposed method on a set of carefully designed simulation data corresponding to multiple situations of true metagenomic data. Results on real data indicate that the uncaptured genomic information can vary dramatically across metagenomic samples, with the potential to mislead downstream analyses. CONCLUSIONS: We proposed the question of how long the total genome length of all different species in a microbial community is and introduced a method to answer it.

Selectivity Switch in a Rhodium(II) Carbene Triggered Cyclopentannulation: Divergent Access to Three Polycyclic Indolines.

A selectivity switch in a RhII /carbene-triggered cyclopentannulation with catalytic InCl3 is reported for the first time, affording both diastereomers of the fused spiroindolines and an unusual bridged tetracyclic indoline in high yields with excellent selectivities. Mechanistic studies indicate an intramolecular annulation of the indole with an in situ formed aminocyclopropane. The stepwise thermal conversions from the kinetic spiroindoline to the metastable bridged indoline, and then to the thermodynamic spiroindoline, involving a ring-opening rearrangement of a cyclopentane, is crucial for selectivity control.

Copper-Catalyzed Enantioselective Cyclopropanation of Internal Olefins with Diazomalonates.

The first enantioselective copper catalyzed cyclopropanation of internal olefins with diazomalonates is reported. This process provides a new method for the synthesis of chiral 1,1-cyclopropane diesters. With a chiral bi-side arm bisoxazoline-copper(I) complex, the reaction performed well over a series of substrates, giving the desired products in good yields (up to 95%) and excellent enantioselectivities (90-95% ee).

Curcumin ameliorates dextran sulfate sodium-induced experimental colitis by blocking STAT3 signaling pathway.

BACKGROUND AND AIMS: Although a series of studies have shown that curcumin can exert anti-inflammatory effects in colitis by inhibiting NF-κB activation, whether these anti-inflammatory effects of curcumin are also attributed to its ability to inhibiting STAT3 pathway has never been tested in experimental colitis to date. The purpose of the study was to investigate whether curcumin could exert its therapeutic effects in experimental colitis by inhibiting STAT3 pathway. MATERIALS AND METHODS: Curcumin was administered in experimental colitis induced by dextran sulfate sodium (DSS). The disease activity index (DAI) and histological score were observed. The phospho-STAT3 was assessed by western blot analysis. The DNA-binding activity of STAT3 dimers was evaluated by electrophoretic mobility shift assay (EMSA). The expression of tumor necrosis factor (TNF)-α and interleukin (IL)-1ß was measured by enzyme-linked immunosorbent assay. Myeloperoxidase (MPO) activity was determined by using MPO assay kit. RESULTS: A significant improvement was observed in DAI and histological score in mice with curcumin, and the increases in phospho-STAT3 activity, DNA-binding activity of STAT3 dimers, MPO activity, IL-1ß, and TNF-α expression in mice with DSS-induced colitis were significantly reduced following treatment with curcumin. CONCLUSION: Curcumin exerts beneficial effects in experimental colitis by the suppression of STAT3 pathway, which may therefore provide a better understanding of the mechanism of action for curcumin in treating colitis.

Acupuncture treatment of children nocturnal enuresis--a report of 56 cases.

OBJECTIVE: To observe the effect of acupuncture in treating nocturnal enuresis in children. METHOD: Shenmen (HT 7) and Weizhong (BL 40) are selected as the main points. Zhongji (CV 3) and Shenshu (BL 23) are added for warming and supplementing the lower origin, and Qihai (CV 6) and Taiyuan (LU 9) for supplementing the middle-jiao and reinforcing the qi, Taichong (LR 3) and Xingjian (LR 2) for clearing away the damp-heat. RESULTS: 56 cases were treated with a total effective rate of 96%. CONCLUSION: Nocturnal enuresis is a condition due to imbalance between the Heart Channel and Bladder Channel. Shenmen (HT 7), a point pertaining to the Heart Channel of Hand-Shaoyin and Weizhong (BL 40), a point pertaining to the Bladder Channel of Foot-Taiyang are selected as the main points, with a good result.

Detection of K-ras gene mutation in fecal samples from elderly large intestinal cancer patients and its diagnostic significance.

AIM: To study the diagnostic significance of K-ras gene mutations in fecal samples from elderly patients with large intestinal cancer. METHODS: DNA was extracted in the fecal and tissue samples from 23 large intestinal cancer patients, 20 colonic adenomatoid polypus patients and 20 healthy subjects. The K-ras gene mutations at the first and second bases of codon 12 were detected by the allele specific mismatch method. RESULTS: The K-ras gene mutation was 56.52%(13/23) in the large intestinal cancer patients, which was notably higher than that in the normal subjects whose K-ras gene mutation was 5%(1/20) (chi (2)=12.93, P<0.001). There was no significant difference in comparison with that of colonic adenomatoid polypus patients whose K-ras gene mutation was 30%(6/12) (chi (2)=3.05, P>0.05). The K-ras gene mutation at the second base of codon 12 was 92.13%(12/13) in the large intestinal cancer patients. There was no significant difference between the detection rate of K-ras gene mutation in the fecal and tissue samples (chi (2)=9.35, P<0.01). CONCLUSION: Our results indicate that detection of the K-ras gene mutations in fecal samples provides a non-invasive diagnostic method for the elderly large intestinal cancer patients. Its significance in the early diagnosis of large intestinal cancer awaits further studies.