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.

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.

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.

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.

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)

Cancer cell-derived exosomal LINC00313 induces M2 macrophage differentiation in non-small cell lung cancer

Purpose Non-small cell lung cancer (NSCLC) is the major subtype of lung cancer, which is the leading cause of cancer death worldwide. Tumor-associated macrophages (TAMs) are one of the main non-tumor cells in the tumor microenvironment. Here, we investigated the effect of cancer cell-derived exosomal LINC00313 on the M2 macrophage differentiation in NSCLC and clarified its underlying mechanism. Methods Flow cytometry, Western blotting, ELISA and immunohistochemical staining were performed to identify the macrophage phenotype by detecting the expression of M2 markers. The expression levels of LINC00313 and miR-135a-3p were measured by qRT-PCR, and luciferase reporter assay was used to validate the binding of lncRNA to miRNA, and miRNA to the target gene STAT6. The mouse-xenograft models were established by subcutaneous injection of the NCl-H1299 cells with stable overexpression or knockdown of LINC00313. GW4869 was injected intra-tumorally after tumor implantation. Results It was found that the cancer cells promoted M2 macrophage differentiation by secreting exosomes. LINC00313 was overexpressed in H1299-derived exosomes, and its knockdown abolished the effect of H1299-induced M2 macrophage differentiation. LINC00313 sponged miR-135a-3p to increase the STAT6 expression, resulting in the M2 macrophage differentiation. LINC00313 promoted tumor progression and promoted the expression of M2 markers in isolated tumor macrophages. A novel regulatory mechanism of M2 macrophage differentiation in NSCLC was revealed. It was found that cancer cell-derived exosomal LINC00313 promoted M2 macrophage differentiation in NSCLC by up-regulating STAT6 as miR-135a-3p sponge. Conclusions This study provides a new mechanism and direction to prevent the M2 macrophage differentiation in NSCLC (AU)

IL-33/ST2 Activation Is involved in Ro60-Regulated Photosensitivity in Cutaneous Lupus Erythematosus.

Interleukin- (IL-) 33 contributes to various inflammatory processes. IL-33/ST2 activation participates in systemic lupus erythematous via binding to the receptor of Suppression of Tumorigenicity 2 protein (ST2). However, whether IL-33/ST2 interferes with the nosogenesis of cutaneous lupus erythematosus (CLE) has not been reported so far. Herein, we proposed to disclose the impacts on IL-33/ST2 activation and Ro60 on CLE and their potential implications in the photosensitization of CLE cells. IL-33, ST2, and Ro60 in CLE patients' skin lesions were detected. Murine keratinocytes stimulated with or without IL-33 were irradiated by ultraviolet B (UVB), and the levels of Ro60 and inflammation markers were determined. Keratinocytes were cocultured with J774.2 macrophages and stimulated with IL-33 for analysis of chemostasis. The results identified that IL-33, ST2, and downstream inflammation markers were significantly upregulated in CLE lesions with Ro60 overexpression. Additionally, IL-33 treatment promoted the upregulation of Ro60 induced by UVB treatment in murine keratinocytes. Moreover, IL-33 stimulates keratinocytes to induce macrophage migration via enhancing the generation of the chemokine (C-C motif) ligands 17 and 22. Meanwhile, the silencing of ST2 or nuclear factor-kappa B (NF-κB) suppression abolished IL-33-induced upregulation of Ro60 in keratinocytes. Similarly, the inhibition of SOX17 expression was followed by downregulation of Ro60 in keratinocytes following IL-33 stimulation. In addition, UVB irradiation upregulated SOX17 in keratinocytes. Conclusively, the IL-33/ST2 axis interferes with Ro60-regulated photosensitization via activating the NF-κB- and PI3K/Akt- and SOX17-related pathways.

Forward single-cell sequencing into clinical application: Understanding of cancer microenvironment at single-cell solution.

Single-cell RNA sequencing (scRNA-seq) is considered an important approach to understand the molecular mechanisms of cancer microenvironmental functions and has the potential for clinical and translational discovery and development. The recent concerns on the impact of scRNA-seq for clinical practice are whether scRNA can be applied as a routine measurement of clinical biochemistry to assist in clinical decision-making for diagnosis and therapy. Pushing single-cell sequencing into clinical application is one of the important missions for clinical and translational medicine (CTM), although there still are a large number of challenges to be overcome. The present Editorial as one of serials aims at overviewing the history of scRNA-seq publications in CTM, sharing the understanding and consideration of the cancer microenvironment at the single-cell solution and emphasising the objective of translating scRNA-seq into clinical application. The dynamic characteristics and patterns of single-cell identity, regulatory networks, and intercellular communication play decisive roles in the properties of the microenvironment, malignancy and migrative capacity of cancer cells, and defensive capacity of immune cells. The microenvironmental single-cell transcriptomic profiles and cell clusters defined by scRNA-seq have great value for exploring the molecular mechanisms of diseases and predicting cell sensitivities to therapy and patient prognosis.

Widespread association of ERα with RMRP and tRNA genes in MCF-7 cells and breast cancers.

tRNA gene transcription by RNA polymerase III (Pol III) is a tightly regulated process, but dysregulated Pol III transcription is widely observed in cancers. Approximately 75% of all breast cancers are positive for expression of Estrogen Receptor alpha (ERα), which acts as a key driver of disease. MCF-7 cells rapidly upregulate tRNA gene transcription in response to estrogen and ChIP-PCR demonstrated ERα enrichment at tRNALeu and 5S rRNA genes in this breast cancer cell line. While these data implicate the ERα as a Pol III transcriptional regulator, how widespread this regulation is across the 631 tRNA genes has yet to be revealed. Through analyses of ERα ChIP-seq datasets, we show that ERα interacts with hundreds of tRNA genes, not only in MCF-7 cells, but also in primary human breast tumours and distant metastases. The extent of ERα association with tRNA genes varies between breast cancer cell lines and does not correlate with levels of ERα binding to its canonical target gene GREB1. Amongst other Pol III-transcribed genes, ERα is consistently enriched at the long non-coding RNA gene RMRP, a positive regulator of cell cycle progression that is subject to focal amplification in tumours. Another Pol III template targeted by ERα is the RN7SL1 gene, which is strongly implicated in breast cancer pathology by inducing inflammatory responses in tumours. Our data indicate that Pol III-transcribed non-coding genes should be added to the list of ERα targets in breast cancer.

Hobit confers tissue-dependent programs to type 1 innate lymphoid cells.

Identification of type 1 innate lymphoid cells (ILC1s) has been problematic. The transcription factor Hobit encoded by Zfp683 has been proposed as a major driver of ILC1 programs. Using Zfp683 reporter mice, we showed that correlation of Hobit expression with ILC1s is tissue- and context-dependent. In liver and intestinal mucosa, Zfp683 expression correlated well with ILC1s; in salivary glands, Zfp683 was coexpressed with the natural killer (NK) master transcription factors Eomes and TCF1 in a unique cell population, which we call ILC1-like NK cells; during viral infection, Zfp683 was induced in conventional NK cells of spleen and liver. The impact of Zfp683 deletion on ILC1s and NK cells was also multifaceted, including a marked decrease in granzyme- and interferon-gamma (IFNγ)-producing ILC1s in the liver, slightly fewer ILC1s and more Eomes+ TCF1+ ILC1-like NK cells in salivary glands, and only reduced production of granzyme B by ILC1 in the intestinal mucosa. NK cell-mediated control of viral infection was unaffected. We conclude that Hobit has two major impacts on ILC1s: It sustains liver ILC1 numbers, while promoting ILC1 functional maturation in other tissues by controlling TCF1, Eomes, and granzyme expression.

A multi-omic single-cell landscape of human gynecologic malignancies.

Deconvolution of regulatory mechanisms that drive transcriptional programs in cancer cells is key to understanding tumor biology. Herein, we present matched transcriptome (scRNA-seq) and chromatin accessibility (scATAC-seq) profiles at single-cell resolution from human ovarian and endometrial tumors processed immediately following surgical resection. This dataset reveals the complex cellular heterogeneity of these tumors and enabled us to quantitatively link variation in chromatin accessibility to gene expression. We show that malignant cells acquire previously unannotated regulatory elements to drive hallmark cancer pathways. Moreover, malignant cells from within the same patients show substantial variation in chromatin accessibility linked to transcriptional output, highlighting the importance of intratumoral heterogeneity. Finally, we infer the malignant cell type-specific activity of transcription factors. By defining the regulatory logic of cancer cells, this work reveals an important reliance on oncogenic regulatory elements and highlights the ability of matched scRNA-seq/scATAC-seq to uncover clinically relevant mechanisms of tumorigenesis in gynecologic cancers.

Quality control in scRNA-Seq can discriminate pacemaker cells: the mtRNA bias.

Single-cell RNA-sequencing (scRNA-seq) provides high-resolution insights into complex tissues. Cardiac tissue, however, poses a major challenge due to the delicate isolation process and the large size of mature cardiomyocytes. Regardless of the experimental technique, captured cells are often impaired and some capture sites may contain multiple or no cells at all. All this refers to "low quality" potentially leading to data misinterpretation. Common standard quality control parameters involve the number of detected genes, transcripts per cell, and the fraction of transcripts from mitochondrial genes. While cutoffs for transcripts and genes per cell are usually user-defined for each experiment or individually calculated, a fixed threshold of 5% mitochondrial transcripts is standard and often set as default in scRNA-seq software. However, this parameter is highly dependent on the tissue type. In the heart, mitochondrial transcripts comprise almost 30% of total mRNA due to high energy demands. Here, we demonstrate that a 5%-threshold not only causes an unacceptable exclusion of cardiomyocytes but also introduces a bias that particularly discriminates pacemaker cells. This effect is apparent for our in vitro generated induced-sinoatrial-bodies (iSABs; highly enriched physiologically functional pacemaker cells), and also evident in a public data set of cells isolated from embryonal murine sinoatrial node tissue (Goodyer William et al. in Circ Res 125:379-397, 2019). Taken together, we recommend omitting this filtering parameter for scRNA-seq in cardiovascular applications whenever possible.

Supervised application of internal validation measures to benchmark dimensionality reduction methods in scRNA-seq data.

A typical single-cell RNA sequencing (scRNA-seq) experiment will measure on the order of 20 000 transcripts and thousands, if not millions, of cells. The high dimensionality of such data presents serious complications for traditional data analysis methods and, as such, methods to reduce dimensionality play an integral role in many analysis pipelines. However, few studies have benchmarked the performance of these methods on scRNA-seq data, with existing comparisons assessing performance via downstream analysis accuracy measures, which may confound the interpretation of their results. Here, we present the most comprehensive benchmark of dimensionality reduction methods in scRNA-seq data to date, utilizing over 300 000 compute hours to assess the performance of over 25 000 low-dimension embeddings across 33 dimensionality reduction methods and 55 scRNA-seq datasets. We employ a simple, yet novel, approach, which does not rely on the results of downstream analyses. Internal validation measures (IVMs), traditionally used as an unsupervised method to assess clustering performance, are repurposed to measure how well-formed biological clusters are after dimensionality reduction. Performance was further evaluated over nearly 200 000 000 iterations of DBSCAN, a density-based clustering algorithm, showing that hyperparameter optimization using IVMs as the objective function leads to near-optimal clustering. Methods were also assessed on the extent to which they preserve the global structure of the data, and on their computational memory and time requirements across a large range of sample sizes. Our comprehensive benchmarking analysis provides a valuable resource for researchers and aims to guide best practice for dimensionality reduction in scRNA-seq analyses, and we highlight Latent Dirichlet Allocation and Potential of Heat-diffusion for Affinity-based Transition Embedding as high-performing algorithms.

CellCall: integrating paired ligand-receptor and transcription factor activities for cell-cell communication.

With the dramatic development of single-cell RNA sequencing (scRNA-seq) technologies, the systematic decoding of cell-cell communication has received great research interest. To date, several in-silico methods have been developed, but most of them lack the ability to predict the communication pathways connecting the insides and outsides of cells. Here, we developed CellCall, a toolkit to infer inter- and intracellular communication pathways by integrating paired ligand-receptor and transcription factor (TF) activity. Moreover, CellCall uses an embedded pathway activity analysis method to identify the significantly activated pathways involved in intercellular crosstalk between certain cell types. Additionally, CellCall offers a rich suite of visualization options (Circos plot, Sankey plot, bubble plot, ridge plot, etc.) to present the analysis results. Case studies on scRNA-seq datasets of human testicular cells and the tumor immune microenvironment demonstrated the reliable and unique functionality of CellCall in intercellular communication analysis and internal TF activity exploration, which were further validated experimentally. Comparative analysis of CellCall and other tools indicated that CellCall was more accurate and offered more functions. In summary, CellCall provides a sophisticated and practical tool allowing researchers to decipher intercellular communication and related internal regulatory signals based on scRNA-seq data. CellCall is freely available at https://github.com/ShellyCoder/cellcall.

Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease.

Mononuclear phagocytes (MNPs) encompass dendritic cells, monocytes, and macrophages (MoMac), which exhibit antimicrobial, homeostatic, and immunoregulatory functions. We integrated 178,651 MNPs from 13 tissues across 41 datasets to generate a MNP single-cell RNA compendium (MNP-VERSE), a publicly available tool to map MNPs and define conserved gene signatures of MNP populations. Next, we generated a MoMac-focused compendium that revealed an array of specialized cell subsets widely distributed across multiple tissues. Specific pathological forms were expanded in cancer and inflammation. All neoplastic tissues contained conserved tumor-associated macrophage populations. In particular, we focused on IL4I1+CD274(PD-L1)+IDO1+ macrophages, which accumulated in the tumor periphery in a T cell-dependent manner via interferon-γ (IFN-γ) and CD40/CD40L-induced maturation from IFN-primed monocytes. IL4I1_Macs exhibited immunosuppressive characteristics through tryptophan degradation and promoted the entry of regulatory T cell into tumors. This integrated analysis provides a robust online-available platform for uniform annotation and dissection of specific macrophage functions in healthy and pathological states.

RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data.

The transcriptomic diversity of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Unsupervised clustering of SC transcriptomes, which is the default technique for defining cell types, is prone to group cells by technical, rather than biological, variation. Compared to de-novo (unsupervised) clustering, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects and data quality artifacts. Here, we present RCA2, the first algorithm to combine reference projection (batch effect robustness) with graph-based clustering (scalability). In addition, RCA2 provides a user-friendly framework incorporating multiple commonly used downstream analysis modules. RCA2 also provides new reference panels for human and mouse and supports generation of custom panels. Furthermore, RCA2 facilitates cell type-specific QC, which is essential for accurate clustering of data from heterogeneous tissues. We demonstrate the advantages of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Scalable supervised clustering methods such as RCA2 will facilitate unified analysis of cohort-scale SC datasets.

scRNA sequencing uncovers a TCF4-dependent transcription factor network regulating commissure development in mouse.

Transcription factor 4 (TCF4) is a crucial regulator of neurodevelopment and has been linked to the pathogenesis of autism, intellectual disability and schizophrenia. As a class I bHLH transcription factor (TF), it is assumed that TCF4 exerts its neurodevelopmental functions through dimerization with proneural class II bHLH TFs. Here, we aim to identify TF partners of TCF4 in the control of interhemispheric connectivity formation. Using a new bioinformatic strategy integrating TF expression levels and regulon activities from single cell RNA-sequencing data, we find evidence that TCF4 interacts with non-bHLH TFs and modulates their transcriptional activity in Satb2+ intercortical projection neurons. Notably, this network comprises regulators linked to the pathogenesis of neurodevelopmental disorders, e.g. FOXG1, SOX11 and BRG1. In support of the functional interaction of TCF4 with non-bHLH TFs, we find that TCF4 and SOX11 biochemically interact and cooperatively control commissure formation in vivo, and regulate the transcription of genes implicated in this process. In addition to identifying new candidate interactors of TCF4 in neurodevelopment, this study illustrates how scRNA-Seq data can be leveraged to predict TF networks in neurodevelopmental processes.

coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data.

Technological advances have enabled us to profile multiple molecular layers at unprecedented single-cell resolution and the available datasets from multiple samples or domains are growing. These datasets, including scRNA-seq data, scATAC-seq data and sc-methylation data, usually have different powers in identifying the unknown cell types through clustering. So, methods that integrate multiple datasets can potentially lead to a better clustering performance. Here we propose coupleCoC+ for the integrative analysis of single-cell genomic data. coupleCoC+ is a transfer learning method based on the information-theoretic co-clustering framework. In coupleCoC+, we utilize the information in one dataset, the source data, to facilitate the analysis of another dataset, the target data. coupleCoC+ uses the linked features in the two datasets for effective knowledge transfer, and it also uses the information of the features in the target data that are unlinked with the source data. In addition, coupleCoC+ matches similar cell types across the source data and the target data. By applying coupleCoC+ to the integrative clustering of mouse cortex scATAC-seq data and scRNA-seq data, mouse and human scRNA-seq data, mouse cortex sc-methylation and scRNA-seq data, and human blood dendritic cells scRNA-seq data from two batches, we demonstrate that coupleCoC+ improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic datasets. coupleCoC+ has fast convergence and it is computationally efficient. The software is available at https://github.com/cuhklinlab/coupleCoC_plus.