Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations.

An integration of 3D chromatin structure and gene expression at single-cell resolution has yet been demonstrated. Here, we develop a computational method, a multiomic data integration (MUDI) algorithm, which integrates scHi-C and scRNA-seq data to precisely define the 3D-regulated and biological-context dependent cell subpopulations or topologically integrated subpopulations (TISPs). We demonstrate its algorithmic utility on the publicly available and newly generated scHi-C and scRNA-seq data. We then test and apply MUDI in a breast cancer cell model system to demonstrate its biological-context dependent utility. We find the newly defined topologically conserved associating domain (CAD) is the characteristic single-cell 3D chromatin structure and better characterizes chromatin domains in single-cell resolution. We further identify 20 TISPs uniquely characterizing 3D-regulated breast cancer cellular states. We reveal two of TISPs are remarkably resemble to high cycling breast cancer persister cells and chromatin modifying enzymes might be functional regulators to drive the alteration of the 3D chromatin structures. Our comprehensive integration of scHi-C and scRNA-seq data in cancer cells at single-cell resolution provides mechanistic insights into 3D-regulated heterogeneity of developing drug-tolerant cancer cells.

Effects of Leydig cell elimination on testicular interstitial cell populations: characterization by scRNA-seq and immunocytochemical techniques.

Background: The mammalian testicular interstitial cells are not well-defined. The present study characterized the interstitial cell types and their turnover dynamics in adult rats. Additionally, the heterogeneity of the mesenchymal population and the effects of Leydig cell elimination on interstitial homeostasis were further analyzed by scRNA-seq datasets and immunocytochemical techniques. Methods: Interstitial cells were defined at the transcriptomic level by scRNA-seq and then confirmed and quantified with protein markers. The dividing activity of the major cell types was determined by continuous EdU labeling of the animals for one week. Some of the rats were also treated with a dose of ethylenedimethylsulfonate (EDS) to examine how the loss of Leydig cells (LCs) could affect interstitial homeostasis for three weeks. Results: Seven interstitial cell types were identified, including cell types (percentage of the whole interstitial population) as follows: Leydig (44.6%), macrophage and dendritic (19.1%), lymphoid (6.2%), vascular endothelial (7.9%), smooth muscle (10.7%), and mesenchymal (11.5%) cells. The EdU experiment indicated that most cell types were dividing at relatively low levels (<9%) except for the mesenchymal cells (MCs, 17.1%). Further analysis of the transcriptome of MCs revealed 4 subgroups with distinct functions, including 1) glutathione metabolism and xenobiotic detoxification, 2) ROS response and AP-1 signaling, 3) extracellular matrix synthesis and binding, and 4) immune response and regulation. Stem LCs (SLCs) are primarily associated with subgroup 3, expressing ARG1 and GAP43. EDS treatment not only eliminated LCs but also increased subgroup 3 and decreased subgroups 1 and 2 of the mesenchymal population. Moreover, EDS treatment increased the division of immune cells by more than tenfold in one week. Conclusion: Seven interstitial cell types were identified and quantified for rat testis. Many may play more diversified roles than previously realized. The elimination of LCs led to significant changes in MCs and immune cells, indicating the importance of LCs in maintaining testicular interstitial homeostasis.

Ro60-Roles in RNA Processing, Inflammation, and Rheumatic Autoimmune Diseases.

The Ro60/SSA2 autoantigen is an RNA-binding protein and a core component of nucleocytoplasmic ribonucleoprotein (RNP) complexes. Ro60 is essential in RNA metabolism, cell stress response pathways, and cellular homeostasis. It stabilises and mediates the quality control and cellular distribution of small RNAs, including YRNAs (for the 'y' in 'cytoplasmic'), retroelement transcripts, and misfolded RNAs. Ro60 transcriptional dysregulation or loss of function can result in the generation and release of RNA fragments from YRNAs and other small RNAs. Small RNA fragments can instigate an inflammatory cascade through endosomal toll-like receptors (TLRs) and cytoplasmic RNA sensors, which typically sense pathogen-associated molecular patterns, and mount the first line of defence against invading pathogens. However, the recognition of host-originating RNA moieties from Ro60 RNP complexes can activate inflammatory response pathways and compromise self-tolerance. Autoreactive B cells may produce antibodies targeting extracellular Ro60 RNP complexes. Ro60 autoantibodies serve as diagnostic markers for various autoimmune diseases, including Sjögren's disease (SjD) and systemic lupus erythematosus (SLE), and they may also act as predictive markers for anti-drug antibody responses among rheumatic patients. Understanding Ro60's structure, function, and role in self-tolerance can enhance our understanding of the underlying molecular mechanisms of autoimmune conditions.

Clinical features associated with the presence of anti-Ro52 and anti-Ro60 antibodies in Jo-1 antibody-positive anti-synthetase syndrome.

Introduction: Anti-SSA antibodies target two unrelated proteins, Ro52 (E3 ligase) and Ro60 (RNA binding protein). Previous studies indicate that anti-Ro52 antibodies are frequently associated with various myositis-specific autoantibodies (MSAs)-including anti-tRNA synthetase antibodies-and that the coexistence of MSAs and anti-Ro52 antibodies may portend worse clinical outcomes. Although not well-described in the setting of myositis, work from our animal model of HRS (histidyl-tRNA synthetase)-induced myositis suggests that anti-Ro60 antibodies may also be linked to specific MSAs such as anti-HRS/Jo-1. We therefore aimed to demonstrate the prevalence and clinical characteristics of Ro52 and Ro60 antibody positivity in patients possessing Jo-1 antibodies. Methods: To establish the immunological link between anti-synthetase, anti-Ro52, and anti-Ro60 antibodies, we evaluated the relative titers of these antibodies in blood and bronchoalveolar lavage fluid (BALF) of mice following immunization with HRS/Jo-1. In parallel, we used ELISA-based approaches to assess sera from 177 anti-Jo1 antibody-positive patients for the presence of anti-Ro52 and/or anti-Ro60 antibodies. We then determined statistical associations between co-existing anti-Jo-1, anti-Ro52, and/or anti-Ro60 antibodies and clinical manifestations associated with the anti-synthetase syndrome. Results: Mice immunized with HRS had higher levels of anti-Ro52 and anti-Ro60 antibodies in serum and BALF than PBS-immunized mice. In 177 anti-Jo-1 antibody-positive patients, the prevalence of anti-Ro52 and anti-Ro60 antibodies was 36% and 15%, respectively. The frequency of dry eye/dry mouth, interstitial pneumonia, and pulmonary events over time differed between patients with various combinations of anti-Ro52 and anti-Ro60 antibodies. While anti-Ro52 antibodies generally correlated with statistically significant increases in each of these clinical manifestations, the presence of Ro60 antibodies alone was associated with decreased frequency of ILD. Discussion: Anti-Ro52 and/or anti-Ro60 antibodies are often co-expressed with anti-Jo1 antibodies, defining clinical subsets with different disease course/outcomes.

Meiotic transcriptional reprogramming mediated by cell-cell communications in humans and mice revealed by scATAC-seq and scRNA-seq.

Meiosis is a highly complex process significantly influenced by transcriptional regulation. However, studies on the mechanisms that govern transcriptomic changes during meiosis, especially in prophase I, are limited. Here, we performed single-cell ATAC-seq of human testis tissues and observed reprogramming during the transition from zygotene to pachytene spermatocytes. This event, conserved in mice, involved the deactivation of genes associated with meiosis after reprogramming and the activation of those related to spermatogenesis before their functional onset. Furthermore, we identified 282 transcriptional regulators (TRs) that underwent activation or deactivation subsequent to this process. Evidence suggested that physical contact signals from Sertoli cells may regulate these TRs in spermatocytes, while secreted ENHO signals may alter metabolic patterns in these cells. Our results further indicated that defective transcriptional reprogramming may be associated with non-obstructive azoospermia (NOA). This study revealed the importance of both physical contact and secreted signals between Sertoli cells and germ cells in meiotic progression.

Systematic mapping of TF-mediated cell fate changes by a pooled induction coupled with scRNA-seq and multi-omics approaches.

Transcriptional regulation controls cellular functions through interactions between transcription factors (TFs) and their chromosomal targets. However, understanding the fate conversion potential of multiple TFs in an inducible manner remains limited. Here, we introduce iTF-seq as a method for identifying individual TFs that can alter cell fate toward specific lineages at a single-cell level. iTF-seq enables time course monitoring of transcriptome changes, and with biotinylated individual TFs, it provides a multi-omics approach to understanding the mechanisms behind TF-mediated cell fate changes. Our iTF-seq study in mouse embryonic stem cells identified multiple TFs that trigger rapid transcriptome changes indicative of differentiation within a day of induction. Moreover, cells expressing these potent TFs often show a slower cell cycle and increased cell death. Further analysis using bioChIP-seq revealed that GCM1 and OTX2 act as pioneer factors and activators by increasing gene accessibility and activating the expression of lineage specification genes during cell fate conversion. iTF-seq has utility in both mapping cell fate conversion and understanding cell fate conversion mechanisms.

scLENS: data-driven signal detection for unbiased scRNA-seq data analysis.

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.

Seronegative patients with primary Sjögren's syndrome and non-pSS sicca test positive for anti-SSA/Ro52 and -Ro60 in saliva.

OBJECTIVES: Testing for anti-SSA/Ro antibodies in serum is essential in the diagnostic work-up for primary Sjögren's syndrome (pSS). In this study, we aimed to validate our previous assay for detection of salivary anti-SSA/Ro52, and to develop assays for detection of salivary anti-SSA/Ro60 and for detection of anti-Ro52 and -Ro60 in plasma using the electric field-induced release and measurement (EFIRM) platform. METHODS: Whole saliva samples from two independent Danish cohorts (DN1 and DN2) including 49 patients with pSS, 73 patients with sicca symptoms, but not fulfilling the classification criteria for pSS (non-pSS sicca), and 51 healthy controls (HC), as well as plasma samples from the DN1 cohort were analyzed using EFIRM to detect anti-SSA/Ro52 and -Ro60. RESULTS: In the DN1 cohort, 100 % in the pSS group and 16 % in the non-pSS sicca group were serum anti-SSA/Ro positive by ELISA. EFIRM detected anti-SSA (Ro52 and/or -Ro60) in plasma and saliva in 100 % and 96 % patients with pSS, and 16 % and 29 % with non-pSS sicca. In the DN2 cohort, 80 % patients with pSS and 26 % with non-pSS sicca were serum anti-SSA/Ro positive. Salivary anti-SSA discriminated patients with pSS from HC and non-pSS sicca with an AUC range of 0.74-0.96 in the DN1 and DN2 cohorts. EFIRM discriminated pSS from non-pSS sicca with an AUC of 0.98 in plasma. CONCLUSION: Our findings suggest that salivary anti-SSA/Ro antibodies are potential discriminatory biomarkers for pSS, which may also identify seronegative patients, addressing the unmet clinical need of early detection and stratification of pSS.

Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data.

In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https://github.com/DayuHuu/scEMC.

Anti-SSA Ro52 and anti-Ro60 autoantibodies: association with clinical phenotypes.

OBJECTIVES: Anti-SSA autoantibodies can be differentiated according to their antigenic target proteins as anti-Ro60 (60 kDa) or anti-Ro52 (52 kDa). Anti-SSA(Ro60) antibodies are clearly associated with connective tissue diseases (CTD), but the clinical significance of anti-SSA(Ro52) antibodies remains unclear. The aim of the present study was to analyse the disease phenotype of patients with anti-Ro52 and/or anti-Ro60 antibodies. METHODS: A multicentre, cross-sectional study was carried out of positive anti-Ro52 and/or Ro60 antibodies patients followed at 10 Rheumatology centres from January 2018 until December 2021. Patients were categorised into 3 groups: group 1 (Ro52+/Ro60-); group 2 (Ro52-/Ro60+); group 3 (Ro52+/Ro60+). Antinuclear antibodies were evaluated by indirect immunofluorescence assay and further screened for anti-extractable nuclear antigen (ENA) antibodies. Demographicsand clinical data were compared between the 3 groups, by patients' medical chart review. Univariate analysis was performed and subsequently logistic regression was used to identify intergroup differences and calculate the odds ratio with a 95% confidence interval (95% CI). RESULTS: We included 776 patients [female: 83.1%; median age: 59 (46-71) years]. Groups 1, 2, and 3 comprised 31.1%, 32.6%, and 36.3% of the patients, respectively. Anti-Ro52 antibody alone was more frequently associated with non-rheumatic diseases, older age, and men (p<0.05). Among patients with CTD, the diagnosis of systemic lupus erythematosus is 3 and 2 times more prevalent in groups 2 and 3, respectively, than in group 1 [OR 2.8 (95% CI 1.60, 4.97), p<0.001; OR 2.2 (95% CI 1.28, 3.86), p<0.01]. In group 2, the diagnosis of undifferentiated CTD is more frequent than in the other groups. Group 1 was more frequently associated with inflammatory myositis than group 2 [OR 0.09 (95% CI 0.01, 0.33), p<0.001] or group 3 [OR 0.08 (95% CI 0.01, 0.29), p<0.001]. Group 1 was also more frequently associated with arthritis (p<0.01), interstitial lung disease (p<0.01), and myositis (p<0.01). CONCLUSIONS: Anti-Ro52+ antibody alone is frequently found in patients with non-rheumatic diseases. In addition, anti-Ro52+ antibody is also prevalent in patients with CTD and associated with clinical phenotypes that are different from anti-Ro60+ antibody.

Circulating mitochondrial long non-coding 7S RNA in primary health care patients with depression/anxiety.

BACKGROUND: The significant role of long non-coding 7S RNA in controlling mitochondrial transcription highlights its importance in mitochondrial function. Considering the suggested connection between mitochondrial dysfunction and the onset of mental disorders, this study aimed to explore the potential involvement of 7S RNA in the context of depression/anxiety. RESULTS: A total of 181 patients in primary health care (age 20-64 years) with depression/anxiety and 59 healthy controls were included in the study. 7S RNA was measured using quantitative real-time PCR in plasma samples collected before (baseline) and after 8 weeks of treatment (mindfulness or cognitive-based behavioral therapy). Upon adjustment for age and sex, the baseline plasma levels of 7S RNA were significantly higher in patients than in healthy controls (p < 0.001). Notably, post-treatment, there was a significant reduction in 7S RNA levels (p = 0.03). These changes in 7S RNA were related to the treatment response, as indicated by HADS-D (Hospital Anxiety and Depression Scale) scores (ß = -0.04, p = 0.04), even after accounting for baseline scores and other cofounders. CONCLUSION: The findings of this study indicate an association between plasma 7S RNA levels and depression/anxiety, as well as treatment response. While further confirmatory analyses are necessary, plasma 7S RNA holds promise as a potential predictive biomarker for both depression/anxiety and the treatment response within these disorders.

Integration of scRNA and bulk RNA-sequence to construct the 5-gene molecular prognostic model based on the heterogeneity of thyroid carcinoma endothelial cell.

Thyroid cancer (TC) is a kind of cancer with high heterogeneity, which leads to significant difference in prognosis. The prognostic molecular processes are not well understood. Cancer cells and tumor microenvironment (TME) cells jointly determine the heterogeneity. However, quite a little attention was paid to cells in the TME in the past years. In this study, we not only reveal that endothelial cells (ECs) are strongly associated with the progress of papillary thyroid cancer (PTC) using single-cell RNA-seq (scRNA-seq) data downloaded from Gene Expression Omnibus (GEO) and WGCNA, but also screen 5 crucial genes of ECs: CLDN5, ABCG2, NOTCH4, PLAT, and TMEM47. Furthermore, the 5-gene molecular prognostic model is constructed, which can predict how well a patient will do on PD-L1 blockade immunotherapy for TC and evaluate prognosis. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis demonstrates that PLAT is decreased in TC and the increase of PLAT can restrain the migratory capacity of TC cells. Meanwhile, in TC cells, PLAT suppresses VEGFa/VEGFR2-mediated human umbilical vascular endothelial cell (HUVEC) proliferation and tube formation. Totally, we construct the 5-gene molecular prognostic model from the perspective of EC and provide a new idea for immunotherapy of TC.

Association of Combined Anti-Ro52/TRIM21 and Anti-Ro60/SSA Antibodies With Increased Sjögren Disease Severity Through Interferon Pathway Activation.

OBJECTIVE: The biologic diagnosis of primary Sjögren disease (SjD) mainly relies on anti-Ro60/SSA antibodies, whereas the significance of anti-Ro52/TRIM21 antibodies currently remains unclear. The aim of this study was to characterize the clinical, serological, biologic, transcriptomic, and interferon profiles of patients with SjD according to their anti-Ro52/TRIM21 antibody status. METHODS: Patients with SjD from the European PRECISESADS (n = 376) and the Brittany Diagnostic Suspicion of primitive Sjögren's Syndrome (DIApSS); (n = 146) cohorts were divided into four groups: double negative (Ro52-/Ro60-), isolated anti-Ro52/TRIM21 positive (Ro52+), isolated anti-Ro60/SSA positive (Ro60+), and double-positive (Ro52+/Ro60+) patients. Clinical information; EULAR Sjögren Syndrome Disease Activity Index, a score representing systemic activity; and biologic markers associated with disease severity were evaluated. Transcriptome data obtained from whole blood by RNA sequencing and type I and II interferon signatures were analyzed for PRECISESADS patients. RESULTS: In the DIApSS cohort, Ro52+/Ro60+ patients showed significantly more parotidomegaly (33.3% vs 0%-11%) along with higher ß2-microglobulin (P = 0.0002), total immunoglobulin (P < 0.0001), and erythrocyte sedimentation rate levels (P = 0.002) as well as rheumatoid factor (RF) positivity (66.2% vs 20.8%-25%) compared to other groups. The PRECISESADS cohort corroborated these observations, with increased arthritis (P = 0.046), inflammation (P = 0.005), hypergammaglobulinemia (P < 0.0001), positive RF (P < 0.0001), leukopenia (P = 0.004), and lymphopenia (P = 0.009) in Ro52+/Ro60+ patients. Cumulative EULAR Sjögren Syndrome Disease Activity Index results further confirmed these disparities (P = 0.002). Transcriptome analysis linked anti-Ro52/TRIM21 antibody positivity to interferon pathway activation as an underlying cause for these clinical correlations. CONCLUSION: These results suggest that the combination of anti-Ro52/TRIM21 and anti-Ro60/SSA antibodies is associated with a clinical, biologic, and transcriptional profile linked to greater disease severity in SjD through the potentiation of the interferon pathway activation by anti-Ro52/TRIM21 antibodies.

ATG9A as a potential diagnostic marker of intervertebral disc degeneration: Inferences from experiments and bioinformatics analysis incorporating sc-RNA-seq data.

BACKGROUND: Disfunctional autophagy plays a pivotal role in Intervertebral Disc Degeneration (IDD) progression. however, the connection between Autophagy-related gene 9A (ATG9A) and IDD has not been reported. METHODS: Firstly, transcriptome datasets from the GEO and Autophagy-related genes (ARGs) from GeneCards were carried out using R. Following this, IDD-specific signature genes were identified through methods such as least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine (SVM) analyses. Validation of these findings proceeded through in vitro experiments, evaluation of independent datasets, and analysis of receiver operating characteristic (ROC) curves. Subsequent steps incorporated co-expression analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA), and construction of competing endogenous RNA (ceRNA) network. The final section established the correlation between immune cell infiltration, ATG9A, and IDD utilizing the CIBERSORT algorithm and single-cell RNA (scRNA) sequencing data. RESULTS: Research identified 87 differentially expressed genes, with only ATG9A noted as an IDD signature gene. Analysis of in vitro experiments and independent datasets uncovered a decrease in ATG9A expression within the degeneration group. The area under the curve (AUC) of ATG9A exceeded 0.8 following ROC analysis. Furthermore, immune cell infiltration and scRNA sequencing data analysis elucidated the substantial role of immune cells in IDD progression. A ceRNA network was constructed, centered around ATG9A, included 4 miRNAs and 22 lncRNAs. CONCLUSION: ATG9A was identified as a diagnostic gene for IDD, indicating its viability as a effective target for therapy disease.

Comparative immunological landscape between pre- and early-stage LUAD manifested as ground-glass nodules revealed by scRNA and scTCR integrated analysis.

BACKGROUND: Mechanism underlying the malignant progression of precancer to early-stage lung adenocarcinoma (LUAD) as well as their indolence nature remains elusive. METHODS: Single-cell RNA sequencing (scRNA) with simultaneous T cell receptor (TCR) sequencing on 5 normal lung tissues, 3 precancerous and 4 early-stage LUAD manifested as pulmonary ground-glass nodules (GGNs) were performed. RESULTS: Through this integrated analysis, we have delineated five key modules that drive the malignant progression of early-stage LUAD in a disease stage-dependent manner. These modules are related to cell proliferation and metabolism, immune response, mitochondria, cilia, and cell adhesion. We also find that the tumor micro-environment (TME) of early-stage LUAD manifested as GGN are featured with regulatory T (Tregs) cells accumulation with three possible origins, and loss-functional state (decreased clonal expansion and cytotoxicity) of CD8 + T cells. Instead of exhaustion, the CD8 + T cells are featured with a shift to memory phenotype, which is significantly different from the late stage LUAD. Furthermore, we have identified monocyte-derived macrophages that undergo a lipid-phenotype transition and may contribute to the suppressive TME. Intense interaction between stromal cells, myeloid cells including lipid associated macrophages and LAMP3 + DCs, and lymphocytes were also characterized. CONCLUSIONS: Our work provides new insight into the molecular and cellular mechanism underlying malignant progression of LUAD manifested as GGN, and pave way for novel immunotherapies for GGN. Video Abstract.

Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures.

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high-dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Here, we propose an interpretable deep learning method called moETM to perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder and employs multiple linear decoders to learn the multi-omics signatures. moETM demonstrates superior performance compared with six state-of-the-art methods on seven publicly available datasets. By applying moETM to the scRNA + scATAC data, we identified sequence motifs corresponding to the transcription factors regulating immune gene signatures. Applying moETM to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omics biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

Detection of a 7SL RNA-derived small non-coding RNA using Molecular Beacons in vitro and in cells.

Small non-coding RNAs (sncRNA) are involved in many steps of the gene expression cascade and regulate processing and expression of mRNAs by the formation of ribonucleoprotein complexes (RNP) such as the RNA-induced silencing complex (RISC). By analyzing small RNA Seq data sets, we identified a sncRNA annotated as piR-hsa-1254, which is likely derived from the 3'-end of 7SL RNA2 (RN7SL2), herein referred to as snc7SL RNA. The 7SL RNA is an abundant long non-coding RNA polymerase III transcript and serves as structural component of the cytoplasmic signal recognition particle (SRP). To evaluate a potential functional role of snc7SL RNA, we aimed to define its cellular localization by live cell imaging. Therefore, a Molecular Beacon (MB)-based method was established to compare the subcellular localization of snc7SL RNA with its precursor 7SL RNA. We designed and characterized several MBs in vitro and tested those by live cell fluorescence microscopy. Using a multiplex approach, we show that 7SL RNA localizes mainly to the endoplasmic reticulum (ER), as expected for the SRP, whereas snc7SL RNA predominately localizes to the nucleus. This finding suggests a fundamentally different function of 7SL RNA and its derivate snc7SL RNA.

Association of Circulating Long Noncoding 7S RNA with Deep Vein Thrombosis.

Mitochondrial dysfunction is a recognized factor in the pathogenesis of deep vein thrombosis (DVT). The role of 7S RNA, a long noncoding RNA that plays an important role in mitochondrial function, in DVT remains unclear. In this study, we aimed to investigate the potential use of 7S RNA as a biomarker in DVT. Plasma samples were obtained from 237 patients (aged 16-95 years) with suspected DVT recruited in a prospective multicenter management study (SCORE) where 53 patients were objectively confirmed with a diagnosis of DVT and the rest were diagnosed as non-DVT. 7S RNA was measured using quantitative real-time polymerase chain reaction in plasma samples. The plasma expression of 7S RNA was significantly lower in DVT compared with non-DVT (0.50 vs. 0.95, p = 0.043). With the linear regression analysis, we showed that the association between the plasma expression of 7S RNA and DVT (ß = -0.72, p = 0.007) was independent of potential confounders. Receiver-operating characteristic curve analysis showed the area under the curve values of 0.60 for 7S RNA. The findings of the present study showed a notable association between 7S RNA and DVT. However, further investigations are needed to fully elucidate the exact role of 7S RNA in the pathophysiology of DVT and its diagnostic value.

Incorporating cell hierarchy to decipher the functional diversity of single cells.

Cells possess functional diversity hierarchically. However, most single-cell analyses neglect the nested structures while detecting and visualizing the functional diversity. Here, we incorporate cell hierarchy to study functional diversity at subpopulation, club (i.e., sub-subpopulation), and cell layers. Accordingly, we implement a package, SEAT, to construct cell hierarchies utilizing structure entropy by minimizing the global uncertainty in cell-cell graphs. With cell hierarchies, SEAT deciphers functional diversity in 36 datasets covering scRNA, scDNA, scATAC, and scRNA-scATAC multiome. First, SEAT finds optimal cell subpopulations with high clustering accuracy. It identifies cell types or fates from omics profiles and boosts accuracy from 0.34 to 1. Second, SEAT detects insightful functional diversity among cell clubs. The hierarchy of breast cancer cells reveals that the specific tumor cell club drives AREG-EGFT signaling. We identify a dense co-accessibility network of cis-regulatory elements specified by one cell club in GM12878. Third, the cell order from the hierarchy infers periodic pseudo-time of cells, improving accuracy from 0.79 to 0.89. Moreover, we incorporate cell hierarchy layers as prior knowledge to refine nonlinear dimension reduction, enabling us to visualize hierarchical cell layouts in low-dimensional space.

The crucial roles of long noncoding RNA SNHGs in lung cancer

Lung cancer is one of the most common malignant tumors with growing morbidity and mortality worldwide. Several treatments are used to manage lung cancer, including surgery, radiotherapy and chemotherapy, as well as molecular-targeted therapy. However, the current measures are still far from satisfactory. Therefore, the current research should focus on exploring the molecular mechanism and then finding an effective treatment. Interestingly, we and others have embarked on a line of investigations focused on the mechanism of lung cancer. Specifically, lncRNA small nucleolar RNA host gene has been shown to be associated with biological characteristics and therapeutic resistance of lung cancer. In addition, small nucleolar RNA host genes may be used as diagnostic biomarker in the future. Herein, we will provide a brief review demonstrating the importance of small nucleolar RNA host genes in lung cancer, especially non-small cell lung cancer. Although lncRNA has shown a crucial role in tumor-related research, a large number of studies are needed to validate its clinical application in the future (AU)