scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model.

MOTIVATION: Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is an important step in revealing cellular heterogeneity. Many clustering methods have been proposed to discover heterogenous cell types from scRNA-seq data. However, adaptive clustering with accurate cluster number reflecting intrinsic biology nature from large-scale scRNA-seq data remains quite challenging. RESULTS: Here, we propose a single-cell Deep Adaptive Clustering (scDAC) model by coupling the Autoencoder (AE) and the Dirichlet Process Mixture Model (DPMM). By jointly optimizing the model parameters of AE and DPMM, scDAC achieves adaptive clustering with accurate cluster numbers on scRNA-seq data. We verify the performance of scDAC on five subsampled datasets with different numbers of cell types and compare it with 15 widely used clustering methods across nine scRNA-seq datasets. Our results demonstrate that scDAC can adaptively find accurate numbers of cell types or subtypes and outperforms other methods. Moreover, the performance of scDAC is robust to hyperparameter changes. AVAILABILITY AND IMPLEMENTATION: The scDAC is implemented in Python. The source code is available at https://github.com/labomics/scDAC.

Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS.

Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas .

Discriminative Geometric-Structure-Based Deep Hashing for Large-Scale Image Retrieval.

Deep hashing reaps the benefits of deep learning and hashing technology, and has become the mainstream of large-scale image retrieval. It generally encodes image into hash code with feature similarity preserving, that is, geometric-structure preservation, and achieves promising retrieval results. In this article, we find that existing geometric-structure preservation manner inadequately ensures feature discrimination, while improving feature discrimination of hash code essentially determines hash learning retrieval performance. This fact principally spurs us to propose a discriminative geometric-structure-based deep hashing method (DGDH), which investigates three novel loss terms based on class centers to induce the so-called discriminative geometrical structure. In detail, the margin-aware center loss assembles samples in the same class to the corresponding class centers for intraclass compactness, then a linear classifier based on class center serves to boost interclass separability, and the radius loss further puts different class centers on a hypersphere to tentatively reduce quantization errors. An efficient alternate optimization algorithm with guaranteed desirable convergence is proposed to optimize DGDH. We theoretically analyze the robustness and generalization of the proposed method. The experiments on five popular benchmark datasets demonstrate superior image retrieval performance of the proposed DGDH over several state of the arts.

Towards Strain-Level Complexity: Sequencing Depth Required for Comprehensive Single-Nucleotide Polymorphism Analysis of the Human Gut Microbiome.

Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp, https://github.com/labomics/SNPsnp). To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.

Revealing diverse alternative splicing variants of the highly homologous SMN1 and SMN2 genes by targeted long-read sequencing.

The survival of motor neuron (SMN) genes, SMN1 and SMN2, are two highly homologous genes related to spinal muscular atrophy (SMA). Different patterns of alternative splicing have been observed in the SMN genes. In this study, the long-read sequencing technique for distinguishing SMN1 and SMN2 without any assembly were developed and applied to reveal multiple alternative splicing patterns and to comprehensively identify transcript variants of the SMN genes. In total, 36 types of transcript variants were identified, with an equal number of variants generated from both SMN1 and SMN2. Of these, 18 were novel SMN transcripts that have never been reported. The structures of SMN transcripts were revealed to be much more complicated and diverse than previously discovered. These novel transcripts were derived from diverse splicing events, including skipping of one or more exons, intron retention, and exon shortening or addition. SMN1 mainly produces FL-SMN1, SMN1Δ7, SMN1Δ5 and SMN1Δ3. The distribution of SMN2 transcripts was significantly different from those of SMN1, with the majority transcripts to be SMN2Δ7, followed by FL-SMN2, SMN2Δ3,5 and SMN2Δ5,7. Targeted long-read sequencing approach could accurately distinguish sequences of SMN1 from those of SMN2. Our study comprehensively addressed naturally occurring SMN1 and SMN2 transcript variants and splicing patterns in peripheral blood mononuclear cells (PBMCs). The novel transcripts identified in our study expanded knowledge of the diversity of transcript variants generated from the SMN genes and showed a much more comprehensive profile of the SMN splicing spectrum. Results in our study will provide valuable information for the study of low expression level of SMN proteins and SMA pathogenesis based on transcript levels.

MetaLogo: a heterogeneity-aware sequence logo generator and aligner.

Sequence logos are used to visually display conservations and variations in short sequences. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators are based on the assumption that all the input sequences are from the same homologous group, which will lead to an overlook of the heterogeneity among the sequences during the sequence logo making process. Heterogeneous groups of sequences may represent clades of different evolutionary origins, or genes families with different functions. Therefore, it is essential to divide the sequences into different phylogenetic or functional groups to reveal their specific sequence motifs and conservation patterns. To solve these problems, we developed MetaLogo, which can automatically cluster the input sequences after multiple sequence alignment and phylogenetic tree construction, and then output sequence logos for multiple groups and aligned them in one figure. User-defined grouping is also supported by MetaLogo to allow users to investigate functional motifs in a more delicate and dynamic perspective. MetaLogo can highlight both the homologous and nonhomologous sites among sequences. MetaLogo can also be used to annotate the evolutionary positions and gene functions of unknown sequences, together with their local sequence characteristics. We provide users a public MetaLogo web server (http://metalogo.omicsnet.org), a standalone Python package (https://github.com/labomics/MetaLogo), and also a built-in web server available for local deployment. Using MetaLogo, users can draw informative, customized and publishable sequence logos without any programming experience to present and investigate new knowledge on specific sequence sets.

Comprehensive Strain-Level Analysis of the Gut Microbe Faecalibacterium prausnitzii in Patients with Liver Cirrhosis.

Liver cirrhosis (LC) has been associated with gut microbes. However, the strain diversity of species and its association with LC have received little attention. Here, we constructed a computational framework to study the strain heterogeneity in the gut microbiome of patients with LC. Only Faecalibacterium prausnitzii shows different single-nucleotide polymorphism (SNP) patterns between the LC and healthy control (HC) groups. Strain diversity analysis discovered that although most F. prausnitzii genomes are more deficient in the LC group than in the HC group at the strain level, a subgroup of 19 F. prausnitzii strains showed no sensitivity to LC, which is inconsistent with the species-level result. The functional differences between this subgroup and other strains may involve short-chain fatty acid production and chlorine-related pathways. These findings demonstrate functional differences among F. prausnitzii subgroups, which extend current knowledge about strain heterogeneity and relationships between F. prausnitzii and LC at the strain level. IMPORTANCE Most metagenomic studies focus on microbes at the species level, thus ignoring the different effects of different strains of the same species on the host. In this study, we explored the different microbes at the strain level in the intestines of patients with liver cirrhosis and of healthy people. Previous studies have shown that the species Faecalibacterium prausnitzii has a lower abundance in patients with liver cirrhosis than in healthy people. However, our results found multiple F. prausnitzii strains that do not decrease in abundance in patients with liver cirrhosis. It is more sensitive to select the appropriate strains as indicators to distinguish between the disease and the control samples than to use the entire species as an indicator. We clustered multiple F. prausnitzii strains and discuss the functional differences of different clusters. Our findings suggest that more attention should be paid to metagenomic studies at the strain level.

Chromosomal microarray analysis in fetuses with high-risk prenatal indications: A retrospective study in China.

OBJECTIVE: The present study aimed to determine the diagnostic value of prenatal chromosomal microarray analysis (CMA) for fetuses with several indications of being at high risk for various conditions. MATERIALS AND METHODS: This retrospective analysis included 1256 pregnancies that were prenatally evaluated due to high-risk indications using invasive CMA. The indications for invasive prenatal diagnosis mainly included ultrasound anomalies, high-risk for maternal serum screening (MSS), high-risk for non-invasive prenatal tests (NIPT), family history of genetic disorders or birth defects, and advanced maternal age (AMA). The rate of clinically significant genomic imbalances between the different groups was compared. RESULTS: The overall prenatal diagnostic yield was 98 (7.8%) of 1256 pregnancies. Clinically significant genomic aberrations were identified in 2 (1.5%) of 132 patients with non-structural ultrasound anomalies, 36 (12.7%) of 283 with structural ultrasound anomalies, 2 (4.5%) of 44 at high-risk for MSS, 38 (26.6%) of 143 at high-risk for NIPT, 11 (3.8%) of 288 with a family history, and 7 (2.1%) of 328 with AMA. Submicroscopic findings were identified in 29 fetuses, 19 of whom showed structural ultrasound anomalies. CONCLUSION: The diagnostic yields of CMA for pregnancies with different indications greatly varied. CMA could serve as a first-tier test for structural anomalies, especially multiple anomalies, craniofacial dysplasia, urinary defects, and cardiac dysplasia. Our results have important implications for genetic counseling.

Integrative single-cell transcriptome analysis reveals a subpopulation of fibroblasts associated with favorable prognosis of liver cancer patients.

Single-cell transcriptome analysis has provided detailed insights into the ecosystem of liver cancer. However, the changes of the cellular and molecular components of liver tumors in comparison with normal livers have not been described at single-cell level. Here, we performed an integrative single-cell analysis of both normal livers and liver cancers. Principal component analysis was firstly performed to delineate the cell lineages in liver tissues. Differential gene expression within major cell types were then analyzed between tumor and normal samples, thus resolved the cell type-specific molecular alterations in liver cancer development. Moreover, a comparison between liver cancer derived versus normal liver derived cell components revealed that two subpopulations of fibroblasts were exclusively expanded in liver cancer tissues. By further defining subpopulation-specific gene signatures, characterizing their spatial distribution in tumor tissues and investigating their clinical significance, we found that the SPARCL1 positive fibroblasts, representing a group of tumor vessel associated fibroblasts, were related to reduced vascular invasion and prolonged survival of liver cancer patients. Through establishing an in-vitro endothelial-to-mesenchymal transition model, we verified the conversion of the fetal liver sinusoidal endothelial cells into the fibroblast-like cells, demonstrating a possible endothelial cell origination of the SPARCL1 positive fibroblasts. Our study provides new insights into the cell atlas alteration, especially the expanded fibroblasts in liver cancers.

Dissecting transcriptional heterogeneity in primary gastric adenocarcinoma by single cell RNA sequencing.

OBJECTIVE: Tumour heterogeneity represents a major obstacle to accurate diagnosis and treatment in gastric adenocarcinoma (GA). Here, we report a systematic transcriptional atlas to delineate molecular and cellular heterogeneity in GA using single-cell RNA sequencing (scRNA-seq). DESIGN: We performed unbiased transcriptome-wide scRNA-seq analysis on 27 677 cells from 9 tumour and 3 non-tumour samples. Analysis results were validated using large-scale histological assays and bulk transcriptomic datasets. RESULTS: Our integrative analysis of tumour cells identified five cell subgroups with distinct expression profiles. A panel of differentiation-related genes reveals a high diversity of differentiation degrees within and between tumours. Low differentiation degrees can predict poor prognosis in GA. Among them, three subgroups exhibited different differentiation grade which corresponded well to histopathological features of Lauren's subtypes. Interestingly, the other two subgroups displayed unique transcriptome features. One subgroup expressing chief-cell markers (eg, LIPF and PGC) and RNF43 with Wnt/ß-catenin signalling pathway activated is consistent with the previously described entity fundic gland-type GA (chief cell-predominant, GA-FG-CCP). We further confirmed the presence of GA-FG-CCP in two public bulk datasets using transcriptomic profiles and histological images. The other subgroup specifically expressed immune-related signature genes (eg, LY6K and major histocompatibility complex class II) with the infection of Epstein-Barr virus. In addition, we also analysed non-malignant epithelium and provided molecular evidences for potential transition from gastric chief cells into MUC6+TFF2+ spasmolytic polypeptide expressing metaplasia. CONCLUSION: Altogether, our study offers valuable resource for deciphering gastric tumour heterogeneity, which will provide assistance for precision diagnosis and prognosis.

Identification of novel SMN1 subtle mutations using an allelic-specific RT-PCR.

Spinal muscular atrophy (SMA) is caused by homozygous deletions of the SMN1 gene in approximately 95% of patients. The remaining 5% of patients with SMA retain at least one copy of the SMN1 gene carrying insertions, deletions, or point mutations. Although molecular genetic testing for most SMA patients is quite easy, diagnosing "nondeletion" SMA patients is still compromised by the presence of a highly homologous SMN2 gene. In this study, we analyzed the SMN1/SMN2 copy number by quantitative PCR and multiplex ligation-dependent probe amplification (MLPA). Further, common primers for both SMN1 and SMN2 sequences were used to screen DNA intragenic mutations. To confirm whether the identified mutations occurred in SMN1 or SMN2, we improved the traditional RT-PCR method by only amplifying SMN1 transcripts using an allelic-specific PCR (AS-RT-PCR) strategy. We identified six SMN1 point mutations and small indels in 8 families, which included c.683T>A, c.22dupA, c.815A>G, c.19delG, c.551_552insA and c.401_402delAG. To the best of our knowledge, the latter three have never been previously reported. The most common mutation in Chinese patients is c.22dupA, which was identified in three families. In this work, we demonstrated AS-RT-PCR to be reliable for identifying SMN1 subtle mutations, especially the prevalent mutation c.22dupA in Chinese SMA patients. By reviewing published papers and summarizing reported SMN1 mutations, a distinct ethnic specificity was found in SMA patients from China. Our research extends the SMN1 mutation spectrum.

Hepatic neddylation targets and stabilizes electron transfer flavoproteins to facilitate fatty acid ß-oxidation.

Neddylation is a ubiquitination-like pathway that controls cell survival and proliferation by covalently conjugating NEDD8 to lysines in specific substrate proteins. However, the physiological role of neddylation in mammalian metabolism remains elusive, and no mitochondrial targets have been identified. Here, we report that mouse models with liver-specific deficiency of NEDD8 or ubiquitin-like modifier activating enzyme 3 (UBA3), the catalytic subunit of the NEDD8-activating enzyme, exhibit neonatal death with spontaneous fatty liver as well as hepatic cellular senescence. In particular, liver-specific UBA3 deficiency leads to systemic abnormalities similar to glutaric aciduria type II (GA-II), a rare autosomal recessive inherited fatty acid oxidation disorder resulting from defects in mitochondrial electron transfer flavoproteins (ETFs: ETFA and ETFB) or the corresponding ubiquinone oxidoreductase. Neddylation inhibition by various strategies results in decreased protein levels of ETFs in neonatal livers and embryonic hepatocytes. Hepatic neddylation also enhances ETF expression in adult mice and prevents fasting-induced steatosis and mortality. Interestingly, neddylation is active in hepatic mitochondria. ETFs are neddylation substrates, and neddylation stabilizes ETFs by inhibiting their ubiquitination and degradation. Moreover, certain mutations of ETFs found in GA-II patients hinder the neddylation of these substrates. Taken together, our results reveal substrates for neddylation and add insight into GA-II.

Susceptibility Factors of Stomach for SARS-CoV-2 and Treatment Implication of Mucosal Protective Agent in COVID-19.

Objectives: This work aims to study the gastrointestinal (GI) symptoms in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients and the susceptibility factors of the stomach for SARS-CoV-2. Materials and Methods: We investigated the SARS-CoV-2 susceptibility by analyzing the expression distribution of viral entry-associated genes, ACE2 and TMPRSS2, in single-cell RNA sequencing data derived from 12 gastric mucosa samples. We also analyzed the epidemiological, demographic, clinical, and laboratory data of 420 cases with SARS-CoV-2-caused coronavirus disease 2019 (COVID-19). Results: ACE2 and TMPRSS2 are specifically expressed in enterocytes which are mainly from gastric mucosa samples with Helicobacter pylori (H. pylori) infection history and intestinal metaplasia (IM). A total of 420 patients were surveyed, of which 62 were with and 358 were without GI symptoms. There is a significant difference in average hospital stay (p < 0.001) and cost (p < 0.001) between the two groups. Among 23 hospitalized patients including seven with upper GI symptoms and 16 with lower GI symptoms, six (85.7%) and five (31.3%) had H. pylori infection history, respectively (p = 0.03). Of 18 hospitalized patients with initial upper GI symptoms, none of the eight patients with mucosal protective agent therapy (e.g., sucralfate suspension gel, hydrotalcite tablets) had diarrhea subsequently, whereas six out of 10 patients without mucosal protective agent therapy had diarrhea subsequently (p = 0.01). Conclusion: IM and H. pylori infection history may be susceptibility factors of SARS-CoV-2, and the mucosal protective agent may be useful for the blockade of SARS-CoV-2 transmission from the stomach to the intestine.

Immune receptor repertoires in pediatric and adult acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML), caused by the abnormal proliferation of immature myeloid cells in the blood or bone marrow, is one of the most common hematologic malignancies. Currently, the interactions between malignant myeloid cells and the immune microenvironment, especially T cells and B cells, remain poorly characterized. METHODS: In this study, we systematically analyzed the T cell receptor and B cell receptor (TCR and BCR) repertoires from the RNA-seq data of 145 pediatric and 151 adult AML samples as well as 73 non-tumor peripheral blood samples. RESULTS: We inferred over 225,000 complementarity-determining region 3 (CDR3) sequences in TCR α, ß, γ, and δ chains and 1,210,000 CDR3 sequences in B cell immunoglobulin (Ig) heavy and light chains. We found higher clonal expansion of both T cells and B cells in the AML microenvironment and observed many differences between pediatric and adult AML. Most notably, adult AML samples have significantly higher level of B cell activation and more secondary Ig class switch events than pediatric AML or non-tumor samples. Furthermore, adult AML with highly expanded IgA2 B cells, which might represent an immunosuppressive microenvironment, are associated with regulatory T cells and worse overall survival. CONCLUSIONS: Our comprehensive characterization of the AML immune receptor repertoires improved our understanding of T cell and B cell immunity in AML, which may provide insights into immunotherapies in hematological malignancies.

[Diagnosis of two cases from one family with Joubert syndrome caused by novel mutations of TCTN1 gene by whole exome sequencing].

OBJECTIVE: To explore the pathogenesis of two fetuses from one family affected with Joubert syndrome (JS). METHODS: Whole exome sequencing was employed to screen potential mutations in both fetuses. Suspected mutations were verified by Sanger sequencing. Impact of intronic mutations on DNA transcription was validated by cDNA analysis. RESULTS: Two novel TCTN1 mutations, c.342-8A>G and c.1494+1G>A, were identified in exons 2 and 12, respectively.cDNA analysis confirmed the pathogenic nature of both mutations with interference of normal splicing resulting in production of truncated proteins. CONCLUSION: The genetic etiology of the family affected with JS has been identified.Above findings have enriched the mutation spectrum of TCTN1gene and facilitated understanding of the genotype-phenotype correlation of JS.

Loss of PICH promotes chromosome instability and cell death in triple-negative breast cancer.

Triple-negative breast cancer (TNBC), defined by the lack of expression of estrogen, progesterone, and ERBB2 receptors, has the worst prognosis of all breast cancers. It is difficult to treat owing to a lack of effective molecular targets. Here, we report that the growth of TNBC cells is exceptionally dependent on PICH, a DNA-dependent ATPase. Clinical samples analysis showed that PICH is highly expressed in TNBC compared to other breast cancer subtypes. Importantly, its high expression correlates with higher risk of distal metastasis and worse clinical outcomes. Further analysis revealed that PICH depletion selectively impairs the proliferation of TNBC cells, but not that of luminal breast cancer cells, in vitro and in vivo. In addition, knockdown of PICH in TNBC cells induces the formation of chromatin bridges and lagging chromosomes in anaphase, frequently resulting in micronucleation or binucleation, finally leading to mitotic catastrophe and apoptosis. Collectively, our findings show the dependency of TNBC cells on PICH for faithful chromosome segregation and the clinical potential of PICH inhibition to improve treatment of patients with high-risk TNBC.

Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model.

BACKGROUND: Compared with clinically functioning pituitary adenoma (FPA), clinically non-functioning pituitary adenoma (NFPA) lacks of detectable hypersecreting serum hormones and related symptoms which make it difficult to predict the prognosis and monitoring for postoperative tumour regrowth. We aim to investigate whether the expression of selected tumour-related proteins and clinical features could be used as tumour markers to effectively predict the regrowth of NFPA. METHOD: Tumour samples were collected from 295 patients with NFPA from Beijing Tiantan Hospital. The expression levels of 41 tumour-associated proteins were assessed using tissue microarray analyses. Clinical characteristics were analysed via univariate and multivariate logistic regression analyses. Logistic regression algorithm was applied to build a prediction model based on the expression levels of selected proteins and clinical signatures, which was then assessed in the testing set. RESULTS: Three proteins and two clinical signatures were confirmed to be significantly related to the regrowth of NFPA, including cyclin-dependent kinase inhibitor 2A (CDKN2A/p16), WNT inhibitory factor 1 (WIF1), tumour growth factor beta (TGF-ß), age and tumour volume. A prediction model was generated on the training set, which achieved a fivefold predictive accuracy of 81.2%. The prediction ability was validated on the testing set with an accuracy of 83.9%. The area under the receiver operating characteristic curves (AUC) for the signatures were 0.895 and 0.881 in the training and testing sets, respectively. CONCLUSION: The prediction model could effectively predict the regrowth of NFPA, which may facilitate the prognostic evaluation and guide early interventions.

Publisher Correction: Landscape of B cell immunity and related immune evasion in human cancers.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.