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.

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.

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.

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.

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.