High-throughput Single-cell RNA Sequencing Research Articles

Determining M2 macrophages content for the anti-tumor effects of metal-organic framework-encapsulated pazopanib nanoparticles in breast cancer

Pazopanib (PAZ), an oral multi-tyrosine kinase inhibitor, demonstrates promising cytostatic activities against various human cancers. However, its clinical utility is limited by substantial side effects and therapeutic resistance. We developed a nanoplatform capable of delivering PAZ for enhanced anti-breast cancer therapy. Nanometer-sized PAZ@Fe-MOF, compared to free PAZ, demonstrated increased anti-tumor therapeutic activities in both syngeneic murine 4T1 and xenograft human MDA-MB-231 breast cancer models. High-throughput single-cell RNA sequencing (scRNAseq) revealed that PAZ@Fe-MOF significantly reduced pro-tumorigenic M2-like macrophage populations at tumor sites and suppressed M2-type signaling pathways, such as ATF6-TGFBR1-SMAD3, as well as chemokines including CCL17, CCL22, and CCL24. PAZ@Fe-MOF reprogramed the inhibitory immune microenvironment and curbed tumorigenicity by blocking the polarization of M2 phenotype macrophages. This platform offers a promising and new strategy for improving the cytotoxicity of PAZ against breast cancers. It provides a method to evaluate the immunological response of tumor cells to PAZ-mediated treatment.

High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding.

Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. The standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R.

A Clustering Method for Single-Cell RNA-Seq Data Based on Automatic Weighting Penalty and Low-Rank Representation.

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) technology have provided more comprehensive biological information on cell expression. Clustering analysis is a critical step in scRNA-seq research and provides clear knowledge of the cell identity. Unfortunately, the characteristics of scRNA-seq data and the limitations of existing technologies make clustering encounter a considerable challenge. Meanwhile, some existing methods treat different features equally and ignore differences in feature contributions, which leads to a loss of information. To overcome limitations, we introduce a weighted distance constraint into the construction of the similarity graph and combine the similarity constraint. We propose the Joint Automatic Weighting Similarity Graph and Low-rank Representation (JAGLRR) clustering method. Evaluating the contributions of each feature and assigning various weight values can increase the significance of valuable features while decreasing the interference of redundant features. The similarity constraint allows the model to generate a more symmetric affinity matrix. Benefitting from that affinity matrix, JAGLRR recovers the original linear relationship of the data more accurately and obtains more discriminative information. The results on simulated datasets and 8 real datasets show that JAGLRR outperforms 11 existing comparison methods in clustering experiments, with higher clustering accuracy and stability.

Senolytic and senomorphic agent procyanidin C1 alleviates structural and functional decline in the aged retina

Increased cellular senescence burden contributes in part to age-related organ dysfunction and pathologies. In our study, using mouse models of natural aging, we observed structural and functional decline in the aged retina, which was accompanied by the accumulation of senescent cells and senescence-associated secretory phenotype factors. We further validated the senolytic and senomorphic properties of procyanidin C1 (PCC1) both in vitro and in vivo, the long-term treatment of which ameliorated age-related retinal impairment. Through high-throughput single-cell RNA sequencing (scRNA-seq), we comprehensively characterized the retinal landscape after PCC1 administration and deciphered the molecular basis underlying the senescence burden increment and elimination. By exploring the scRNA-seq database of age-related retinal disorders, we revealed the role of cellular senescence and the therapeutic potential of PCC1 in these pathologies. Overall, these results indicate the therapeutic effects of PCC1 on the aged retina and its potential use for treating age-related retinal disorders.

Evolutionary and developmental specialization of foveal cell types in the marmoset

In primates, high-acuity vision is mediated by the fovea, a small specialized central region of the retina. The fovea, unique to the anthropoid lineage among mammals, undergoes notable neuronal morphological changes during postnatal maturation. However, the extent of cellular similarity across anthropoid foveas and the molecular underpinnings of foveal maturation remain unclear. Here, we used high-throughput single-cell RNA sequencing to profile retinal cells of the common marmoset (Callithrix jacchus), an early divergent in anthropoid evolution from humans, apes, and macaques. We generated atlases of the marmoset fovea and peripheral retina for both neonates and adults. Our comparative analysis revealed that marmosets share almost all their foveal types with both humans and macaques, highlighting a conserved cellular structure among primate foveas. Furthermore, by tracing the developmental trajectory of cell types in the foveal and peripheral retina, we found distinct maturation paths for each. In-depth analysis of gene expression differences demonstrated that cone photoreceptors and Müller glia (MG), among others, show the greatest molecular divergence between these two regions. Utilizing single-cell ATAC-seq and gene-regulatory network inference, we uncovered distinct transcriptional regulations differentiating foveal cones from their peripheral counterparts. Further analysis of predicted ligand-receptor interactions suggested a potential role for MG in supporting the maturation of foveal cones. Together, these results provide valuable insights into foveal development, structure, and evolution.

Comparative analysis of 10X Chromium vs. BD Rhapsody whole transcriptome single-cell sequencing technologies in complex human tissues

The development of single-cell omics tools has enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, each of the different techniques may have its unique strengths and limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium vs. microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy.Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of cell populations differed. Cells with low mRNA content such as T cells were underrepresented in the droplet-based system, at least partly due to lower RNA capture rates. In contrast, microwell-based scRNA-seq recovered less cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation. Overall, our study provides important information for selection of the appropriate scRNA-seq platform and for the interpretation of published results.

High-Throughput Host-Microbe Single-Cell RNA Sequencing Reveals Ferroptosis-Associated Heterogeneity during Acinetobacter baumannii Infection.

Interactions between host and bacterial cells are integral to human physiology. The complexity of host-microbe interactions extends to different cell types, spatial aspects, and phenotypic heterogeneity, requiring high-resolution approaches to capture their full complexity. The latest breakthroughs in single-cell RNA sequencing (scRNA-seq) have opened up a new era of studies in host-pathogen interactions. Here, we first report a high-throughput cross-species dual scRNA-seq technology by using random primers to simultaneously capture both eukaryotic and bacterial RNAs (scRandom-seq). Using reference cells, scRandom-seq can detect individual eukaryotic and bacterial cells with high throughput and high specificity. Acinetobacter baumannii (A.b) is a highly opportunistic and nosocomial pathogen that displays resistance to many antibiotics, posing a significant threat to human health, calling for discoveries and treatment. In the A.b infection model, scRandom-seq witnessed polarization of THP-1 derived-macrophages and the intracellular A.b-induced ferroptosis-stress in host cells. The inhibition of ferroptosis by Ferrostatin-1 (Fer-1) resulted in the improvement of cell vitality and resistance to A.b infection, indicating the potential to resist related infections. scRandom-seq provides a high-throughput cross-species dual single-cell RNA profiling tool that will facilitate future discoveries in unraveling the complex interactions of host-microbe interactions in infection systems and tumor micro-environments.

Bulk and Single-Cell RNA Sequencing Elucidate the Etiology of Severe COVID-19.

Coronavirus disease 2019 (COVID-19) is a type of pneumonia caused by the SARS-CoV-2 coronavirus. It can cause acute pulmonary and systemic inflammation, which can lead to death in severely ill patients. This study explores the potential reasons behind severe COVID-19 and its similarities to systemic autoimmune diseases. This study reviewed unbiased high-throughput gene expression datasets, including next-generation and single-cell RNA sequencing. A total of 27 studies and eight meta-analyses were reviewed. The studies indicated that severe COVID-19 is associated with the upregulation of genes involved in pro-inflammatory, interferon, and cytokine/chemokine pathways. Additionally, changes were observed in the proportions of immune cell types in the blood and tissues, along with degenerative alterations in lung epithelial cells. Genomic evidence also supports the association of severe COVID-19 with various inflammatory syndromes, such as neuronal COVID-19, acute respiratory distress syndrome, vascular inflammation, and multisystem inflammatory syndrome. In conclusion, this study suggests that gene expression profiling plays a significant role in elucidating the etiology of severe COVID-19.

A comprehensive database of exosome molecular biomarkers and disease-gene associations

Exosomes play a crucial role in intercellular communication and can be used as biomarkers for diagnostic and therapeutic clinical applications. However, systematic studies in cancer-associated exosomal nucleic acids remain a big challenge. Here, we developed ExMdb, a comprehensive database of exosomal nucleic acid biomarkers and disease-gene associations curated from published literature and high-throughput datasets. We performed a comprehensive curation of exosome properties including 4,586 experimentally supported gene-disease associations, 13,768 diagnostic and therapeutic biomarkers, and 312,049 nucleic acid subcellular locations. To characterize expression variation of exosomal molecules and identify causal factors of complex diseases, we have also collected 164 high-throughput datasets, including bulk and single-cell RNA sequencing (scRNA-seq) data. Based on these datasets, we performed various bioinformatics and statistical analyses to support our conclusions and advance our knowledge of exosome biology. Collectively, our dataset will serve as an essential resource for investigating the regulatory mechanisms of complex diseases and improving the development of diagnostic and therapeutic biomarkers.

Analytical Workflows for Single-Cell Multiomic Data Using the BD Rhapsody Platform.

The conversion of raw sequencing reads to biologically relevant data in high-throughput single-cell RNA sequencing experiments is a complex and involved process. Drawing meaning from thousands of individual cells to provide biological insight requires ensuring not only that the data are of the highest quality but also that the signal can be separated from noise. In this article, we describe a detailed analytical workflow, including six pipelines, that allows high-quality data analysis in single-cell multiomics. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Image analysis Basic Protocol 2: Sequencing quality control and generation of a gene expression matrix Basic Protocol 3: Gene expression matrix data pre-processing and analysis Basic Protocol 4: Advanced analysis Basic Protocol 5: Conversion to flow cytometry standard (FCS) format Basic Protocol 6: Visualization using graphical interfaces.

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts.

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single-cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

Long-read single-cell sequencing reveals the transcriptional landscape of spermatogenesis in obstructive azoospermia and Sertoli cell-only patients.

High-throughput single-cell RNA sequencing (scRNA-seq) is widely used in spermatogenesis. However, it only reveals short reads in germ and somatic cells, limiting the discovery of novel transcripts and genes. This study shows the long-read transcriptional landscape of spermatogenesis in obstructive azoospermia (OA) and Sertoli cell-only patients. Single cells were isolated from testicular biopsies of OA and non-obstructive azoospermia (NOA) patients. Cell culture was identified by comparing PacBio long-read single-cell sequencing (OA n = 3, NOA n = 3) with short-read scRNA-seq (OA n = 6, NOA n = 6). Ten germ cell types and eight somatic cell types were classified based on known markers. PacBio long-read single-cell sequencing, short-read scRNA-seq, polymerase chain reaction. A total of 130 426 long-read transcripts (100 517 novel transcripts and 29 909 known transcripts) and 49 508 long-read transcripts (26 002 novel transcripts and 23 506 known transcripts) have been detected in OA and NOA patients, respectively. Moreover, 36 373 and 1642 new genes are identified in OA and NOA patients, respectively. Importantly, specific expressions of long-read transcripts were detected in germ and stomatic cells during normal spermatogenesis. We have identified total full-length transcripts in OA and NOA, and new genes were found. Furthermore, specific expressed full-length transcripts were detected, and the genomic structure of transcripts was mapped in different cell types. These findings may provide valuable information on human spermatogenesis and the treatment of male infertility.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis.

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer

Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 were not previously reported. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-β/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.

Single-cell transcriptomics in thyroid eye disease

Abstract Thyroid eye disease (TED) is a poorly understood autoimmune condition affecting the retroorbital tissue. Tissue inflammation, expansion, and fibrosis can potentially lead to debilitating sequelae such as vision loss, painful eye movement, proptosis, and eyelid retraction. Current treatment modalities for TED include systemic glucocorticoids, thioamides, methimazole, teprotumumab, beta-blockers, and radioactive iodine; however, it has been reported that up to 10%–20% of TED patients relapse after treatment withdrawal and 20%–30% are unresponsive to mainstay therapy for reasons that have yet to be more clearly elucidated. In the past 4 years, vision researchers have harnessed high-throughput single-cell RNA sequencing to elucidate the diversity of cell types and molecular mechanisms driving the pathogenesis of TED at single-cell resolution. Such studies have provided unprecedented insight regarding novel biomarkers and therapeutic targets in TED. This timely review summarizes recent breakthroughs and emerging opportunities for using single-cell and single-nuclei transcriptomic data to characterize this highly complex disease state. We also provide an overview of current challenges and future applications of this technology to potentially improve patient quality of life and facilitate reversal of disease endpoints.

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data. The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.

A targeted sequencing extension for transcript genotyping in single-cell transcriptomics.

As no existing methods within the single-cell RNA sequencing repertoire combine genotyping of specific genomic loci with high throughput, we evaluated a straightforward, targeted sequencing approach as an extension to high-throughput droplet-based single-cell RNA sequencing. Overlaying standard gene expression data with transcript level genotype information provides a strategy to study the impact of genetic variants. Here, we describe this targeted sequencing extension, explain how to process the data and evaluate how technical parameters such as amount of input cDNA, number of amplification rounds, and sequencing depth influence the number of transcripts detected. Finally, we demonstrate how targeted sequencing can be used in two contexts: (1) simultaneous investigation of the presence of a somatic variant and its potential impact on the transcriptome of affected cells and (2) evaluation of allele-specific expression of a germline variant in ad hoc cell subsets. Through these and other comparable applications, our targeted sequencing extension has the potential to improve our understanding of functional effects caused by genetic variation.

Immune landscape in rejection of renal transplantation revealed by high-throughput single-cell RNA sequencing.

Background: The role of the cellular level in kidney transplant rejection is unclear, and single-cell RNA sequencing (scRNA-seq) can reveal the single-cell landscape behind rejection of human kidney allografts at the single-cell level. Methods: High-quality transcriptomes were generated from scRNA-seq data from five human kidney transplantation biopsy cores. Cluster analysis was performed on the scRNA-seq data by known cell marker genes in order to identify different cell types. In addition, pathways, pseudotime developmental trajectories and transcriptional regulatory networks involved in different cell subpopulations were explored. Next, we systematically analyzed the scoring of gene sets regarding single-cell expression profiles based on biological processes associated with oxidative stress. Results: We obtained 81,139 single cells by scRNA-seq from kidney transplant tissue biopsies of three antibody-mediated rejection (ABMR) patients and two acute kidney injury (AKI) patients with non-rejection causes and identified 11 cell types, including immune cells, renal cells and several stromal cells. Immune cells such as macrophages showed inflammatory activation and antigen presentation and complement signaling, especially in rejection where some subpopulations of cells specifically expressed in rejection showed specific pro-inflammatory responses. In addition, patients with rejection are characterized by an increased number of fibroblasts, and further analysis of subpopulations of fibroblasts revealed their involvement in inflammatory and fibrosis-related pathways leading to increased renal rejection and fibrosis. Notably, the gene set score for response to oxidative stress was higher in patients with rejection. Conclusion: Insight into histological differences in kidney transplant patients with or without rejection was gained by assessing differences in cellular levels at single-cell resolution. In conclusion, we applied scRNA-seq to rejection after renal transplantation to deconstruct its heterogeneity and identify new targets for personalized therapeutic approaches.

P-739 High-throughput Single-cell Survey Reveal a Pathological Niche in Developing Down Syndrome Testis

Abstract Study question What are the characteristics of developing testes in DS, and how do these alterations affect gamete development and androgen production? Summary answer Molecular alterations start accumulating in every cell-type of DS fetal testis. Different cell components, compromised functions, and deviated cellular communications make up a pathological niche. What is known already DS is the most common genetic chromosomal disorder. The flat or slightly decreased live birth and the significantly prolonged lifespan contribute to a growing DS population. Their needs for reproductive care come into view. Reproductive health is crucial for spermatogenesis, male characteristics, and even brain behaviors. DS males experience normal puberty, but their sex hormones and associated gonadotropins have altered since the infant, implying testis niche cell dysfunction. Male infertility may result from germ cell and/or niche cell malfunction. A single-cell resolution survey could help thoroughly uncover the heterogeneity of DS by cell type. Study design, size, duration We applied high-throughput single-cell RNA sequencing to capture the transcriptomes of DS testis (n = 1). In parallel, testis collected from the fetus reported no genetic disorders were set as control (n = 1). Molecular and histological verifications were conducted with independent samples (3 versus 3). Samples enrolled were around 22 weeks of gestation. Participants/materials, setting, methods With ethics approval and informed consent obtainment, DS and control samples were included. DS fetus was confirmed with amniocentesis, while no genetic abnormality was reported during the prenatal examination of the diploid fetus. Single-cell RNA sequencing was conducted with the Micro-well platform, pseudotime trajectory analysis, cellular interaction analysis, and weighted gene co-expression network analysis were performed. Transmission electron microscopy, immunohistochemistry, Westerns, and qPCR were used to verify the main differences. Main results and the role of chance A total of 15349 cell transcriptomes, DS and control pooled, were compared and clustered. Major cell types were unabridged in both karyotypes. DS testis contains more Sertoli cells with an atypical expression profile, and more immune cells. DS cells exhibit a much higher level of ribosomal protein (RP genes and a significantlylower level of marker genes for each cell type, such as INSL3 for DS Leydig cell and PECAM1 for DS endothelial cell, etc. Corresponding to cell proportion difference, gene co-expression network and intercellular communication analysis also suggested an immune activation in DS. Furthermore, pseudotime analysis suggested a relative retardation and malfunction of DS cells. The main differences in gene expression were further verified. Cell proportion alteration was demonstrated with immunohistochemistry. Overwhelming RPs were shown by transmission electron microscopy. Western and qPCR were used to test the expression difference of RPs, cell-type specific functional genes, etc. Limitations, reasons for caution The sample size was limited, although verification experiments were applied with independent samples might increase the credibility. The sample size needs to be extended further. More precise molecular mechanisms of these alterations and clinical dysfunction remains to be elucidate. Wider implications of the findings This study described human fetal DS testis at single-cell level. It reveals a pathological niche of altered cell components, compromised cell function, and activated immune response. It provides further targets for complement and/or intervention to rescue the reproductive function and even improve brain behavior in DS patients, soon after birth. Trial registration number Not applicable

LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.

From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data. In this article, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and the elastic net regression model is adopted to fit the binarized time series data. Then, the estimated regression coefficients are applied to represent the unknown Boolean threshold function of the candidate Boolean threshold network as the dynamical equations. To overcome the multi-collinearity and over-fitting problems, a new and effective approach is designed to optimize the network topology by adding a perturbation design matrix to the input data and thereafter setting sufficiently small elements of the output coefficient vector to zeros. In addition, the cross-validation procedure is implemented into the Boolean threshold network model framework to strengthen the inference capability. Finally, extensive experiments on one simulated Boolean value dataset, dozens of simulation datasets, and three real single-cell RNA sequencing datasets demonstrate that the LogBTF method can infer GRNs from time series data more accurately than some other alternative methods for GRN inference. The source data and code are available at https://github.com/zpliulab/LogBTF.