Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments

Barcodes based on nucleic acid sequences: Applications and challenges (Review)

Cells are the fundamental structural and functional units of living organisms and the study of these entities has remained a central focus throughout the history of biological sciences. Traditional cell research techniques, including fluorescent protein tagging and microscopy, have provided preliminary insights into the lineage history and clonal relationships between progenitor and descendant cells. However, these techniques exhibit inherent limitations in tracking the full developmental trajectory of cells and elucidating their heterogeneity, including sensitivity, stability and barcode drift. In developmental biology, nucleic acid barcode technology has introduced an innovative approach to cell lineage tracing. By assigning unique barcodes to individual cells, researchers can accurately identify and trace the origin and differentiation pathways of cells at various developmental stages, thereby illuminating the dynamic processes underlying tissue development and organogenesis. In cancer research, nucleic acid barcoding has played a pivotal role in analyzing the clonal architecture of tumor cells, exploring their heterogeneity and resistance mechanisms and enhancing our understanding of cancer evolution and inter‑clonal interactions. Furthermore, nucleic acid barcodes play a crucial role in stem cell research, enabling the tracking of stem cells from diverse origins and their derived progeny. This has offered novel perspectives on the mechanisms of stem cell self‑renewal and differentiation. The present review presented a comprehensive examination of the principles, applications and challenges associated with nucleic acid barcode technology.

SciGeneX: enhancing transcriptional analysis through gene module detection in single-cell and spatial transcriptomics data

The standard pipeline to analyze single-cell RNA-seq or spatial transcriptomics data focuses on a gene-centric approach that overlooks the collective behavior of genes. However, understanding cell populations necessitates recognizing intricate combinations of activated and repressed pathways. Therefore, a broader view of gene behavior offers more accurate insights into cellular heterogeneity in single-cell or spatial transcriptomics data. Here, we describe SciGeneX (Single-cell informative Gene eXplorer), a R package implementing a neighborhood analysis and a graph partitioning method to generate co-expression gene modules. These modules, whether shared or restricted to cell populations, collectively reflect cellular heterogeneity. Their combinations are able to highlight specific cell populations, even rare ones. SciGeneX uncovers rare and novel cell populations that were not observed before in human thymus spatial transcriptomics data. We show that SciGeneX outperforms existing methods on both artificial and experimental datasets. Overall, SciGeneX will aid in unravelling cellular and molecular diversity in single-cell and spatial transcriptomics studies.

hiPSC-neurons recapitulate the subtype-specific cell intrinsic nature of susceptibility to neurodegenerative disease-relevant aggregation

Alzheimer's disease (AD) is characterized by the accumulation and spread of Tau intraneuronal inclusions throughout most of the telencephalon, leaving hindbrain regions like the cerebellum and spinal cord largely spared. These neuropathological observations, along with the identification of specific vulnerable sub-populations from AD brain-derived single nuclei transcriptomics, suggest that a subset of brain regions and neuronal subtypes possess a selective vulnerability to Tau pathology. Given the inability to culture neurons from patient brains, a disease-relevant in vitro model which recapitulates these features would serve as a critical tool to validate modulators of vulnerability and resilience. Using our recently established platform for inducing endogenous Tau aggregation in human induced pluripotent stem cell (hiPSC)-derived cortical excitatory neurons via application of AD brain-derived exogenous Tau aggregates, we explored whether Tau aggregates preferentially induce aggregation in specific neuronal subtypes. We compared Tau seeding in hiPSC-derived neuron subtypes representing regional identities across the forebrain, midbrain, and hindbrain. Higher susceptibility (i.e. more Tau aggregation) was consistently observed among cortical neuron subtypes, with CTIP2-positive, somatostatin (SST)-positive cortical inhibitory neurons showing the greatest aggregation levels across hiPSC lines from multiple donors. hiPSC-neurons also delineated between the disease-specific vulnerabilities of different protein aggregates, as α-synuclein preformed fibrils showed an increased propensity to induce aggregates in midbrain dopaminergic (mDA)-like neurons, mimicking Parkinson's disease (PD)-specific susceptibility. Aggregate uptake and degradation rates were insufficient to explain differential susceptibility. The absence of a consistent transcriptional response following aggregate seeding further indicated that intrinsic neuronal subtype-specific properties could drive susceptibility. The present data provides evidence that hiPSC-neurons exhibit features of selective neuronal vulnerability which manifest in a cell autonomous manner, suggesting that mining intrinsic (or basal) transcriptomic signatures of more vulnerable compared to more resilient hiPSC-neurons could uncover the molecular underpinnings of differential susceptibility to protein aggregation found in a variety of neurodegenerative diseases.

Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications

Single-cell RNA-sequencing (scRNA-seq) technology brought about a revolutionary change in the transcriptomic world, paving the way for comprehensive analysis of cellular heterogeneity in complex biological systems. It enabled researchers to see how different cells behaved at single-cell levels, providing new insights into the process. However, despite all these advancements, scRNA-seq also experiences challenges related to the complexity of data analysis, interpretation, and multi-omics data integration. In this review, these complications were discussed in detail, directly pointing at the optimization of scRNA-seq approaches and understanding the world of single-cell and its dynamics. Different protocols and currently functional single-cell databases were also covered. This review highlights different tools for the analysis of scRNA-seq and their methodologies, emphasizing innovative techniques that enhance resolution and accuracy at a single-cell level. Various applications were explored across domains including drug discovery, tumor microenvironment (TME), biomarker discovery, and microbial profiling, and case studies were discussed to explain the importance of scRNA-seq by uncovering novel and rare cell types and their identification. This review underlines a crucial aspect of scRNA-seq in the advancement of personalized medicine and highlights its potential to understand the complexity of biological systems.

Single-cell analysis reveals the implication of vascular endothelial cell-intrinsic ANGPT2 in human intracranial aneurysm

AIMS

While previous single-cell RNA sequencing (scRNA-seq) studies have attempted to dissect intracranial aneurysm (IA), the primary molecular mechanism for IA pathogenesis remains unknown. Here, we uncovered the alterations of cellular compositions, especially the transcriptome changes of vascular endothelial cells (ECs), in human IA.

METHODS AND RESULTS

We performed scRNA-seq to compare the cell atlas of sporadic IA and the control artery. The transcriptomes of 43 462 cells were profiled for further analysis. In general, IA had increased immune cells (T/NK cells, B cells, myeloid cells, mast cells, neutrophils) and fewer vascular cells (ECs, vascular smooth muscle cells, and fibroblasts). Based on the obtained high-quantity and high-quality EC data, we found genes associated with angiogenesis in ECs from IA patients. By EC-specific expression of candidate genes in vivo, we observed the involvement of angpt2a in causing cerebral vascular abnormality. Furthermore, an IA zebrafish model mimicking the main features of human IA was generated through targeting pdgfrb gene, and knockdown of angpt2a alleviated the vascular dilation in the IA zebrafish model.

CONCLUSION

By performing a landscape view of the single-cell transcriptomes of IA and the control artery, we contribute to a deeper understanding of the cellular composition and the molecular changes of ECs in IA. The implication of angiogenic regulator ANGPT2 in IA formation and progression, provides a novel potential therapeutical target for IA interventions.

Limitations and optimizations of cellular lineages tracking

Tracking cellular lineages using genetic barcodes provides insights across biology and has become an important tool. However, barcoding strategies remain ad hoc. We show that elevating barcode insertion probability and thus increasing the average number of barcodes within the cells, adds to the number of traceable lineages but may decrease the accuracy of lineages inference due to reading errors. We establish the trade-off between accuracy in tracing lineages and the total number of traceable lineages, and find optimal experimental parameters under limited resources concerning the populations size of tracked cells and barcode pool complexity.

High resolution tissue and cell type identification via single cell transcriptomic profiling

Tissue identification can be instrumental in reconstructing a crime scene but remains a challenging task in forensic investigations. Conventionally, identifying the presence of certain tissue from tissue mixture by predefined cell type markers in bulk fashion is challenging due to limitations in sensitivity and accuracy. In contrast, single-cell RNA sequencing (scRNA-Seq) is a promising technology that has the potential to enhance or even revolutionize tissue and cell type identification. In this study, we developed a high sensitive general purpose single cell annotation pipeline, scTissueID, to accurately evaluate the single cell profile quality and precisely determine the cell and tissue types based on scRNA profiles. By incorporating a crucial and unique reference cell quality differentiation phase of targeting only high confident cells as reference, scTissueID achieved better and consistent performance in determining cell and tissue types compared to 8 state-of-art single cell annotation pipelines and 6 widely adopted machine learning algorithms, as demonstrated through a large-scale and comprehensive comparison study using both forensic-relevant and Human Cell Atlas (HCA) data. We highlighted the significance of cell quality differentiation, a previously undervalued factor. Thus, this study offers a tool capable of accurately and efficiently identifying cell and tissue types, with broad applicability to forensic investigations and other biomedical research endeavors.

uN2CpolyG-mediated p65 nuclear sequestration suppresses the NF-κB-NLRP3 pathway in neuronal intranuclear inclusion disease

BACKGROUND

Neuronal intranuclear inclusion disease (NIID) is genetically linked to CGG repeat expansion in the 5'-untranslated region of the NOTCH2NLC gene, with nascent polyglycine-containing protein (uN2CpolyG) identified as a primary pathogenic factor. Emerging clinical evidence suggests that inflammation contributes to NIID pathogenesis, yet the underlying molecular mechanisms remain elusive. This study aimed to elucidate the molecular interaction between uN2CpolyG and the NF-κB-NLRP3 pathway.

METHODS

Single-cell RNA sequencing was conducted on the skin tissues of NIID patients to assess changes in the expression of genes involved in inflammatory pathways. Cell models (HEK-293T and U87-MG) transfected with CGG9/69/100 expansion vectors were used to investigate alterations in the NF-κB-NLRP3-autophagy pathway. Additionally, the therapeutic potential of NF-κB activators was evaluated in a Drosophila model with a CGG expansion knock-in.

RESULTS

Single-cell sequencing revealed a significant reduction in the expression of NFKBIA, encoding NF-κB inhibitor alpha (IkBa), which facilitates the nuclear translocation of p65, a key NF-κB component. uN2CpolyG directly interacted with and sequestered p65 in nuclear inclusions, leading to reduced phosphorylated p65 (p-p65) levels. This sequestration significantly downregulated the NF-κB-NLRP3 pathway, impairing autophagy, as indicated by decreased LC3II/LC3I ratios. Treatment of CGG100 cells with lipopolysaccharide (LPS) significantly increased p-p65, NLRP3, and LC3II/LC3I levels while reducing insoluble uN2CpolyG levels and intranuclear inclusions. In the Drosophila knock-in model, LPS significantly reduced the number of intranuclear inclusions and improved phenotypic manifestations.

CONCLUSIONS

This study revealed that uN2CpolyG directly interacts with and sequesters p65, thereby inhibiting the NF-κB-NLRP3 pathway and impairing autophagy. This mechanism highlights a novel therapeutic target for NIID and provides potentially broader insights into similar mechanisms in other neurodegenerative diseases characterized by misfolded protein aggregates.

A Dynamic Shift in Estrogen Receptor Expression During Granulosa Cell Differentiation in the Ovary

This study uncovers a dynamic shift in estrogen receptor expression during granulosa cell (GC) differentiation in the ovary, highlighting a transition from estrogen receptor alpha (ESR1) to estrogen receptor beta (ESR2). Using a transgenic mouse model with Esr1-iCre-mediated Esr2 deletion, we demonstrate that ESR2 expression is absent in GCs derived from ESR1-expressing ovarian surface epithelium (OSE) cells. Single-cell analysis of the OSE-GC lineage reveals a developmental trajectory from Esr1-expressing OSE cells to Foxl2-expressing pre-GCs, culminating in GCs exclusively expressing Esr2. Transcriptome analyses identified vasculature-derived TGFβ1 ligands as key regulators of this transition. Supporting this, TGFβ1 treatment of cultured embryonic ovaries reduced Esr1 expression while promoting Esr2 expression. This study underscores the capability of GCs to switch from ESR1 to ESR2 expression as a fundamental aspect of normal differentiation.

Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses

Double dipping is a well-known pitfall in single-cell and spatial transcriptomics data analysis: after a clustering algorithm finds clusters as putative cell types or spatial domains, statistical tests are applied to the same data to identify differentially expressed (DE) genes as potential cell-type or spatial-domain markers. Because the genes that contribute to clustering are inherently likely to be identified as DE genes, double dipping can result in false-positive cell-type or spatial-domain markers, especially when clusters are spurious, leading to ambiguously defined cell types or spatial domains. To address this challenge, we propose ClusterDE, a statistical method designed to identify post-clustering DE genes as reliable markers of cell types and spatial domains, while controlling the false discovery rate (FDR) regardless of clustering quality. The core of ClusterDE involves generating synthetic null data as an in silico negative control that contains only one cell type or spatial domain, allowing for the detection and removal of spurious discoveries caused by double dipping. We demonstrate that ClusterDE controls the FDR and identifies canonical cell-type and spatial-domain markers as top DE genes, distinguishing them from housekeeping genes. ClusterDE's ability to discover reliable markers, or the absence of such markers, can be used to determine whether two ambiguous clusters should be merged. Additionally, ClusterDE is compatible with state-of-the-art analysis pipelines like Seurat and Scanpy.

Identification of driving genes of recurrent miscarriage based on transcriptome sequencing and immunoinfiltration analysis

AIMS

Recurrent miscarriage (RM) plagues 1 %-5 % women of childbearing age. Facing the limitations of clinical treatment, its pathological mechanism remains to be clarified.

METHODS

Decidual tissues of three induced abortions and three RM were collected for transcriptome sequencing. The pathological features of RM were identified by differential expression genes (DEGs) analysis, GSEA, GO and KEGG analysis, and a protein-protein interaction network was constructed for DEGs, and six algorithms were used to identify hub genes. In addition, the immune characteristics of RM patients were identified by CIBERSORT, and the correlation between them and hub genes was analyzed. Furthermore, in single-cell level, different cells were grouped according to the expression level of hub genes, and the expression ratio and abundance of hub genes in different cells and their regulation on cell function were explored.

RESULTS

Transcriptome sequencing of patients with RM showed that a large number of genes were down-regulated, which was related to fibroblast proliferation, epithelial cell migration, female pregnancy and cell chemotaxis. Fifteen hub genes were identified by constructing a protein-protein interaction network, among which DUSP1, NR4A1 and THBS1 were involved in cell migration and chemotaxis. Immune cell infiltration analysis showed that the infiltration of T cells, macrophages and NK cells was abnormal, and there was a significant correlation with hub genes. Moreover, we found that compared with the expression of DUSP1, the non-expression of DUSP1 will reduce the extracellular matrix formation of fibroblasts and the chemotaxis of macrophages. At the same time, it is worth noting that the expression ratio and abundance of hub genes are decreased in epithelial cells, fibroblasts, macrophages and NK cells. Furthermore, single-cell analysis and in vitro and in vivo experiments show that DUSP1 and NR4A1 are low-expressed in different cells of RM patients, which is accompanied by the inhibition of fibroblast proliferation and macrophage chemotaxis. Drug prediction and screening based on hub genes show that Cinobufagin and calmidazolium are expected to be candidate drugs for RM.

CONCLUSION

Hub genes such as DUSP1, NR4A1 and THBS1 participate in RM by regulating epithelial cell migration, fibroblast proliferation and macrophage chemotaxis, which will provide new insight for the diagnosis and targeted therapy of RM.

Sample multiplexing for retinal single-cell RNA sequencing

Rare cell populations can be challenging to characterize using microfluidic single-cell RNA sequencing (scRNA-seq) platforms. Typically, the population of interest must be enriched and pooled from multiple biological specimens for efficient collection. However, these practices preclude the resolution of sample origin together with phenotypic data and are problematic in experiments in which biological or technical variation is expected to be high (e.g., disease models, genetic perturbation screens, or human samples). One solution is sample multiplexing whereby each sample is tagged with a unique sequence barcode that is resolved bioinformatically. We have established a scRNA-seq sample multiplexing pipeline for mouse retinal ganglion cells using cholesterol-modified oligos. We utilized the enhanced precision of this dataset to investigate cell type distribution and transcriptomic variance across retinal samples. Additionally, we demonstrate that our multiplexed dataset can be useful for the identification of multiplets in non-labeled samples, a common challenge in scRNA-seq analysis.

Origin of Ewing sarcoma by embryonic reprogramming of neural crest to mesoderm

Ewing sarcoma is a malignant small round blue cell tumor of bones and soft tissues caused by chromosomal translocations that generate aberrant fusion oncogenes, most frequently EWSR1::FLI1. The cell of origin and mechanisms of EWSR1::FLI1-driven transformation have remained unresolved, largely due to lack of a representative animal model. By developing a zebrafish Ewing sarcoma model, we provide evidence for a neural crest origin of this cancer. Neural crest-derived cells uniquely tolerate expression of EWSR1::FLI1 and targeted expression of EWSR1::FLI1 in these cells generates Ewing sarcomas. Single-cell analysis of tumor initiation shows that EWSR1::FLI1 reprograms neural crest-derived cells to a mesoderm-like state, strikingly resulting in ectopic fins throughout the body. By profiling chromatin accessibility and genome-wide EWSR1::FLI1 binding, we find that the fusion oncogene hijacks developmental enhancers for neural crest to mesoderm reprogramming during cancer initiation. These findings show how a single mutation profoundly alters embryonic cell fate decisions to initiate a devastating childhood cancer.

Single-cell transcriptomics reveals neural stem cell trans-differentiation and cell subpopulations in whole heart decellularized extracellular matrix

The whole heart decellularized extracellular matrix (ECM) has become a promising scaffold material for cardiac tissue engineering. Our previous research has shown that the whole heart acellular matrix possesses the memory function regulating neural stem cells (NSCs) trans-differentiating to cardiac lineage cells. However, the cell subpopulations and phenotypes in the trans-differentiation of NSCs have not been clearly identified. Here, we performed single-cell RNA sequencing and identified 2,765 cells in the recellularized heart with NSCs revealing the cellular diversity of cardiac and neural lineage, confirming NSCs were capable of trans-differentiating into the cardiac lineage while maintaining the original ability to differentiate into the neural lineage. Notably, the trans-differentiated heart-like cells have dual signatures of neuroectoderm and cardiac mesoderm. This study unveils an in-depth mechanism underlying the trans-differentiation of NSCs and provides a new opportunity and theoretical basis for cardiac regeneration.

Aquaporin 1 aggravates lipopolysaccharide-induced macrophage polarization and pyroptosis

Acute respiratory infections (ARIs) are associated with high mortality and morbidity. Acute lung injury (ALI) is caused by the activation of immune cells during ARIs caused by viruses such as SARS-CoV-2. Aquaporin 1 (AQP1) is distributed in a variety of immune cells and is related to the occurrence of ALI, but the mechanism is not clear. A reference map of human single cells was used to identify macrophages in COVID-19 patients at the single-cell level. "FindMarkers" was used to analyze differentially expressed genes (DEGs), and "clusterProfiler" was used to analyze the functions of the DEGs. An M1 macrophage polarization model was established with lipopolysaccharide (LPS) in vitro, and the relationships among AQP1, pyroptosis and M1 polarization were examined by using an AQP1 inhibitor. Transcriptome sequencing and RT-qPCR were used to examine the molecular mechanism by which AQP1 regulates macrophage polarization and pyroptosis. Antigen presentation, M1 polarization, migration and phagocytosis are abnormal in SARS-CoV-2-infected macrophages, which is related to the high expression of AQP1. An M1 polarization model of macrophages was constructed in vitro, and an AQP1 inhibitor was used to examine whether AQP1 could promote M1 polarization and pyroptosis in response to LPS. Transcriptome and cell experiments showed that this effect was related to a decrease in chemokines caused by AQP1 deficiency. AQP1 participates in M1 polarization and pyroptosis in macrophages by increasing the levels of chemokines induced by LPS, which provides new insights for the diagnosis and treatment of ALI.

Varied cellular abnormalities in thin vs. normal endometrium in recurrent implantation failure by single-cell transcriptomics

BACKGROUND

Reduced endometrium thickness and receptivity are two important reasons for recurrent implantation failure (RIF). In order to elucidate differences between these two types of endometrial defects in terms of molecular signatures, cellular interactions, and structural changes, we systematically investigated the single-cell transcriptomic atlas across three distinct groups: RIF patients with thin endometrium (≤ 6 mm, TE-RIF), RIF patients with normal endometrium thickness (≥ 8 mm, NE-RIF), and fertile individuals (Control).

METHODS

The late proliferative and mid-secretory phases of the endometrium were collected from three individuals in the TE-RIF group, two in the NE-RIF group, and three in the control group. The study employed a combination of advanced techniques. Single-cell RNA sequencing (scRNA-seq) was utilized to capture comprehensive transcriptomic profiles at the single-cell level, providing insights into gene expression patterns within specific cell types. Scanning and transmission electron microscopy were employed to visualize ultrastructural details of the endometrial tissue, while hematoxylin and eosin staining facilitated the examination of tissue morphology and cellular composition. Immunohistochemistry techniques were also applied to detect and localize specific protein markers relevant to endometrial receptivity and function.

RESULTS

Through comparative analysis of differentially expressed genes among these groups and KEGG pathway analysis, the TE-RIF group exhibited notable dysregulations in the TNF and MAPK signaling pathways, which are pivotal in stromal cell growth and endometrial receptivity. Conversely, in the NE-RIF group, disturbances in energy metabolism emerged as a primary contributor to reduced endometrial receptivity. Additionally, using CellPhoneDB for intercellular communication analysis revealed aberrant interactions between epithelial and stromal cells, impacting endometrial receptivity specifically in the TE-RIF group.

CONCLUSION

Overall, our findings provide valuable insights into the heterogeneous molecular pathways and cellular interactions associated with RIF in different endometrial conditions. These insights may pave the way for targeted therapeutic interventions aimed at improving endometrial receptivity and enhancing reproductive outcomes in patients undergoing ART. Further research is warranted to validate these findings and translate them into clinical applications for personalized fertility treatments.

TRIAL REGISTRATION

Not applicable.

Timing and location dictate monocyte fate and their transition to tumor-associated macrophages

Tumor-associated macrophages (TAMs) are a heterogeneous population of cells whose phenotypes and functions are shaped by factors that are incompletely understood. Herein, we asked when and where TAMs arise from blood monocytes and how they evolve during tumor development. We initiated pancreatic ductal adenocarcinoma (PDAC) in inducible monocyte fate-mapping mice and combined single-cell transcriptomics and high-dimensional flow cytometry to profile the monocyte-to-TAM transition. We revealed that monocytes differentiate first into a transient intermediate population of TAMs that generates two longer-lived lineages of terminally differentiated TAMs with distinct gene expression profiles, phenotypes, and intratumoral localization. Transcriptome datasets and tumor samples from patients with PDAC evidenced parallel TAM populations in humans and their prognostic associations. These insights will support the design of new therapeutic strategies targeting TAMs in PDAC.

Macrophages preserve endothelial cell specialization in the adrenal gland to modulate aldosterone secretion and blood pressure

Macrophages play crucial roles in organ-specific functions and homeostasis. In the adrenal gland, macrophages closely associate with sinusoidal capillaries in the aldosterone-producing zona glomerulosa. We demonstrate that macrophages preserve capillary specialization and modulate aldosterone secretion. Using macrophage-specific deletion of VEGF-A, single-cell transcriptomics, and functional phenotyping, we found that the loss of VEGF-A depletes PLVAP+ fenestrated endothelial cells in the zona glomerulosa, leading to increased basement membrane collagen IV deposition and subendothelial fibrosis. This results in increased aldosterone secretion, called "haptosecretagogue" signaling. Human aldosterone-producing adenomas also show capillary rarefaction and basement membrane thickening. Mice with myeloid cell-specific VEGF-A deletion exhibit elevated serum aldosterone, hypokalemia, and hypertension, mimicking primary aldosteronism. These findings underscore macrophage-to-endothelial cell signaling as essential for endothelial cell specialization, adrenal gland function, and blood pressure regulation, with broader implications for other endocrine organs.

Myeloid progenitor dysregulation fuels immunosuppressive macrophages in tumors

Monocyte-derived macrophages (mo-macs) drive immunosuppression in the tumor microenvironment (TME) and tumor-enhanced myelopoiesis in the bone marrow (BM) fuels these populations. Here, we performed paired transcriptome and chromatin analysis over the continuum of BM myeloid progenitors, circulating monocytes, and tumor-infiltrating mo-macs in mice and in patients with lung cancer to identify myeloid progenitor programs that fuel pro-tumorigenic mo-macs. Analyzing chromatin accessibility and histone mark changes, we show that lung tumors prime accessibility for Nfe2l2 (NRF2) in BM myeloid progenitors as a cytoprotective response to oxidative stress. NRF2 activity is sustained and increased during monocyte differentiation into mo-macs in the lung TME to regulate oxidative stress, in turn promoting metabolic adaptation, resistance to cell death, and contributing to immunosuppressive phenotype. NRF2 genetic deletion and pharmacological inhibition significantly reduced mo-macs' survival and immunosuppression in the TME, enabling NK and T cell therapeutic antitumor immunity and synergizing with checkpoint blockade strategies. Altogether, our study identifies a targetable epigenetic node of myeloid progenitor dysregulation that sustains immunoregulatory mo-macs in the TME.

Identify Regulatory eQTLs by Multiome Sequencing in Prostate Single Cells

While genome-wide association studies and expression quantitative trait loci (eQTL) analysis have made significant progress in identifying noncoding variants associated with prostate cancer risk and bulk tissue transcriptome changes, the regulatory effect of these genetic elements on gene expression remains largely unknown. Recent developments in single-cell sequencing have made it possible to perform ATAC-seq and RNA-seq profiling simultaneously to capture functional associations between chromatin accessibility and gene expression. In this study, we tested our hypothesis that this multiome single-cell approach allows for mapping regulatory elements and their target genes at prostate cancer risk loci. We applied a 10X Multiome ATAC + Gene Expression platform to encapsulate Tn5 transposase-tagged nuclei from multiple prostate cell lines for a total of 65,501 high quality single cells from RWPE1, RWPE2, PrEC, BPH1, DU145, PC3, 22Rv1 and LNCaP cell lines. To address data sparsity commonly seen in the single-cell sequencing, we performed targeted sequencing to enrich sequencing data at prostate cancer risk loci involving 2,730 candidate germline variants and 273 associated genes. Although not increasing the number of captured cells, the targeted multiome data did improve eQTL gene expression abundance by about 20% and chromatin accessibility abundance by about 5%. Based on this multiomic profiling, we further associated RNA expression alterations with chromatin accessibility of germline variants at single cell levels. Cross validation analysis showed high overlaps between the multiome associations and the bulk eQTL findings from GTEx prostate cohort. We found that about 20% of GTEx eQTLs were covered within the significant multiome associations (p-value ≤ 0.05, gene abundance percentage ≥ 5%), and roughly 10% of the multiome associations could be identified by significant GTEx eQTLs. We also analyzed accessible regions with available heterozygous SNP reads and observed more frequent association in genomic regions with allelically accessible variants (p = 0.0055). Among these findings were previously reported regulatory variants including rs60464856-RUVBL1 (multiome p-value = 0.0099 in BPH1) and rs7247241-SPINT2 (multiome p-value = 0.0002- 0.0004 in 22Rv1). We also functionally validated a new regulatory SNP and its target gene rs2474694-VPS53 (multiome p-value = 0.00956 in BPH1 and 0.00625 in DU145) by reporter assay and SILAC proteomics sequencing. Taken together, our data demonstrated the feasibility of the multiome single-cell approach for identifying regulatory SNPs and their regulated genes.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

SifiNet: a robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multi-omic cellular profiles. It is conveniently available as an open-source R package.

Age-Dependent RGS5 Loss in Pericytes Induces Cardiac Dysfunction and Fibrosis

BACKGROUND

Pericytes are capillary-associated mural cells involved in the maintenance and stability of the vascular network. Although aging is one of the main risk factors for cardiovascular disease, the consequences of aging on cardiac pericytes are unknown.

METHODS

In this study, we have combined single-nucleus RNA sequencing and histological analysis to determine the effects of aging on cardiac pericytes. Furthermore, we have conducted in vivo and in vitro analysis of RGS5 (regulator of G-protein signaling 5) loss of function and finally have performed pericytes-fibroblasts coculture studies to understand the effect of RGS5 deletion in pericytes on the neighboring fibroblasts.

RESULTS

Aging reduced the pericyte area and capillary coverage in the murine heart. Single-nucleus RNA sequencing analysis further revealed that the expression of Rgs5 was reduced in cardiac pericytes from aged mice. In vivo and in vitro studies showed that the deletion of RGS5 impaired cardiac function, induced fibrosis, and morphological changes in pericytes characterized by a profibrotic gene expression signature and the expression of different ECM (extracellular matrix) components and growth factors, for example, TGFB2 and PDGFB. Indeed, culturing fibroblasts with the supernatant of RGS5-deficient pericytes induced their activation as evidenced by the increased expression of αSMA (alpha smooth muscle actin) in a TGFβ (transforming growth factor beta)2-dependent mechanism.

CONCLUSIONS

Our results have identified RGS5 as a crucial regulator of pericyte function during cardiac aging. The deletion of RGS5 causes cardiac dysfunction and induces myocardial fibrosis, one of the hallmarks of cardiac aging.

Sample multiplexing for retinal single-cell RNA-sequencing

Rare cell populations can be challenging to characterize using microfluidic single-cell RNA sequencing (scRNA-seq) platforms. Typically, the population of interest must be enriched and pooled from multiple biological specimens for efficient collection. However, these practices preclude the resolution of sample origin together with phenotypic data and are problematic in experiments in which biological or technical variation is expected to be high (e.g., disease models, genetic perturbation screens, or human samples). One solution is sample multiplexing whereby each sample is tagged with a unique sequence barcode that is resolved bioinformatically. We have established a scRNA-seq sample multiplexing pipeline for mouse retinal ganglion cells using cholesterol-modified-oligos and utilized the enhanced precision to investigate cell type distribution and transcriptomic variance across retinal samples. As single cell transcriptomics are becoming more widely used to research development and disease, sample multiplexing represents a useful method to enhance the precision of scRNA-seq analysis.

A comparison of marker gene selection methods for single-cell RNA sequencing data

BACKGROUND

The development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalog and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper, we benchmark 59 computational methods for selecting marker genes in scRNA-seq data.

RESULTS

We compare the performance of the methods using 14 real scRNA-seq datasets and over 170 additional simulated datasets. Methods are compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed, and their implementation quality. In addition, various case studies are used to scrutinize the most commonly used methods, highlighting issues and inconsistencies.

CONCLUSIONS

Overall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Student's t-test, and logistic regression.

A novel batch-effect correction method for scRNA-seq data based on Adversarial Information Factorization

Single-cell RNA sequencing (scRNA-seq) technology produces an unprecedented resolution at the level of a unique cell, raising great hopes in medicine. Nevertheless, scRNA-seq data suffer from high variations due to the experimental conditions, called batch effects, preventing any aggregated downstream analysis. Adversarial Information Factorization provides a robust batch-effect correction method that does not rely on prior knowledge of the cell types nor a specific normalization strategy while being adapted to any downstream analysis task. It compares to and even outperforms state-of-the-art methods in several scenarios: low signal-to-noise ratio, batch-specific cell types with few cells, and a multi-batches dataset with imbalanced batches and batch-specific cell types. Moreover, it best preserves the relative gene expression between cell types, yielding superior differential expression analysis results. Finally, in a more complex setting of a Leukemia cohort, our method preserved most of the underlying biological information for each patient while aligning the batches, improving the clustering metrics in the aggregated dataset.

Tick innate immune responses to hematophagy and Ehrlichia infection at single-cell resolution

Introduction

Ticks rely on robust cellular and humoral responses to control microbial infection. However, several aspects of the tick's innate immune system remain uncharacterized, most notably that of the immune cells (called hemocytes), which are known to play a significant role in cellular and humoral responses. Despite the importance of hemocytes in regulating microbial infection, our understanding of their basic biology and molecular mechanisms remains limited. Therefore, we believe that a more detailed understanding of the role of hemocytes in the interactions between ticks and tick-borne microbes is crucial to illuminating their function in vector competence and to help identify novel targets for developing new strategies to block tick-borne pathogen transmission.

Methods

This study examined hemocytes from the lone star tick (Amblyomma americanum) at the transcriptomic level using the 10X genomics single-cell RNA sequencing platform to analyze hemocyte populations from unfed, partially blood-fed, and Ehrlichia chaffeensis-infected ticks. The functional role of differentially expressed hemocyte markers in hemocyte proliferation and Ehrlichia dissemination was determined using an RNA interference approach.

Results and discussion

Our data exhibit the identification of fourteen distinct hemocyte populations. Our results uncover seven distinct lineages present in uninfected and Ehrlichia-infected hemocyte clusters. The functional characterization of hemocytin, cystatin, fibronectin, and lipocalin demonstrate their role in hemocyte population changes, proliferation, and Ehrlichia dissemination.

Conclusion

Our results uncover the tick immune responses to Ehrlichia infection and hematophagy at a single-cell resolution. This work opens a new field of tick innate immunobiology to understand the role of hemocytes, particularly in response to prolonged blood-feeding (hematophagy), and tick-microbial interactions.

sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer's Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.

Imputation for Single-cell RNA-seq Data with Non-negative Matrix Factorization and Transfer Learning

Single-cell RNA sequencing (scRNA-seq) has been proven to be an effective technology for investigating the heterogeneity and transcriptome dynamics due to the single-cell resolution. However, one of the major problems for data obtained by scRNA-seq is excessive zeros in the count matrix, which hinders the downstream analysis enormously. Here, we present a method that integrates non-negative matrix factorization and transfer learning (NMFTL) to impute the scRNA-seq data. It borrows gene expression information from the additional dataset and adds graph-regularized terms to the decomposed matrices. These strategies not only maintain the intrinsic geometrical structure of the data itself but also further improve the accuracy of estimating the expression values by adding the transfer term in the model. The real data analysis result demonstrates that the proposed method outperforms the existing matrix-factorization-based imputation methods in recovering dropout entries, preserving gene-to-gene and cell-to-cell relationships, and in the downstream analysis, such as cell clustering analysis, the proposed method also has a good performance. For convenience, we have implemented the "NMFTL" method with R scripts, which could be available at https://github.com/FocusPaka/NMFTL.

Semi-reference based cell type deconvolution with application to human metastatic cancers

Bulk RNA-seq experiments, commonly used to discern gene expression changes across conditions, often neglect critical cell type-specific information due to their focus on average transcript abundance. Recognizing cell type contribution is crucial to understanding phenotype and disease variations. The advent of single-cell RNA sequencing has allowed detailed examination of cellular heterogeneity; however, the cost and analytic caveat prohibits such sequencing for a large number of samples. We introduce a novel deconvolution approach, SECRET, that employs cell type-specific gene expression profiles from single-cell RNA-seq to accurately estimate cell type proportions from bulk RNA-seq data. Notably, SECRET can adapt to scenarios where the cell type present in the bulk data is unrepresented in the reference, thereby offering increased flexibility in reference selection. SECRET has demonstrated superior accuracy compared to existing methods using synthetic data and has identified unknown tissue-specific cell types in real human metastatic cancers. Its versatility makes it broadly applicable across various human cancer studies.

Dbh+ catecholaminergic cardiomyocytes contribute to the structure and function of the cardiac conduction system in murine heart

The heterogeneity of functional cardiomyocytes arises during heart development, which is essential to the complex and highly coordinated cardiac physiological function. Yet the biological and physiological identities and the origin of the specialized cardiomyocyte populations have not been fully comprehended. Here we report a previously unrecognised population of cardiomyocytes expressing Dbhgene encoding dopamine beta-hydroxylase in murine heart. We determined how these myocytes are distributed across the heart by utilising advanced single-cell and spatial transcriptomic analyses, genetic fate mapping and molecular imaging with computational reconstruction. We demonstrated that they form the key functional components of the cardiac conduction system by using optogenetic electrophysiology and conditional cardiomyocyte Dbh gene deletion models. We revealed their close relationship with sympathetic innervation during cardiac conduction system formation. Our study thus provides new insights into the development and heterogeneity of the mammalian cardiac conduction system by revealing a new cardiomyocyte population with potential catecholaminergic endocrine function.

SCALA: A complete solution for multimodal analysis of single-cell Next Generation Sequencing data

Analysis and interpretation of high-throughput transcriptional and chromatin accessibility data at single-cell (sc) resolution are still open challenges in the biomedical field. The existence of countless bioinformatics tools, for the different analytical steps, increases the complexity of data interpretation and the difficulty to derive biological insights. In this article, we present SCALA, a bioinformatics tool for analysis and visualization of single-cell RNA sequencing (scRNA-seq) and Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) datasets, enabling either independent or integrative analysis of the two modalities. SCALA combines standard types of analysis by integrating multiple software packages varying from quality control to the identification of distinct cell populations and cell states. Additional analysis options enable functional enrichment, cellular trajectory inference, ligand-receptor analysis, and regulatory network reconstruction. SCALA is fully parameterizable, presenting data in tabular format and producing publication-ready visualizations. The different available analysis modules can aid biomedical researchers in exploring, analyzing, and visualizing their data without any prior experience in coding. We demonstrate the functionality of SCALA through two use-cases related to TNF-driven arthritic mice, handling both scRNA-seq and scATAC-seq datasets. SCALA is developed in R, Shiny and JavaScript and is mainly available as a standalone version, while an online service of more limited capacity can be found at http://scala.pavlopouloslab.info or https://scala.fleming.gr.

Hepatocyte CYR61 polarizes profibrotic macrophages to orchestrate NASH fibrosis

Obesity is increasing worldwide and leads to a multitude of metabolic diseases, including cardiovascular disease, type 2 diabetes, nonalcoholic fatty liver disease, and nonalcoholic steatohepatitis (NASH). Cysteine-rich angiogenic inducer 61 (CYR61) is associated with the progression of NASH, but it has been described to have anti- and proinflammatory properties. We sought to examine the role of liver CYR61 in NASH progression. CYR61 liver-specific knockout mice on a NASH diet showed improved glucose tolerance, decreased liver inflammation, and reduced fibrosis. CYR61 polarized infiltrating monocytes promoting a proinflammatory/profibrotic phenotype through an IRAK4/SYK/NF-κB signaling cascade. In vitro, CYR61 activated a profibrotic program, including PDGFa/PDGFb expression in macrophages, in an IRAK4/SYK/NF-κB-dependent manner. Furthermore, targeted-antibody blockade reduced CYR61-driven signaling in macrophages in vitro and in vivo, reducing fibrotic development. This study demonstrates that CYR61 is a key driver of liver inflammation and fibrosis in NASH.

scMultiGAN: cell-specific imputation for single-cell transcriptomes with multiple deep generative adversarial networks

The emergence of single-cell RNA sequencing (scRNA-seq) technology has revolutionized the identification of cell types and the study of cellular states at a single-cell level. Despite its significant potential, scRNA-seq data analysis is plagued by the issue of missing values. Many existing imputation methods rely on simplistic data distribution assumptions while ignoring the intrinsic gene expression distribution specific to cells. This work presents a novel deep-learning model, named scMultiGAN, for scRNA-seq imputation, which utilizes multiple collaborative generative adversarial networks (GAN). Unlike traditional GAN-based imputation methods that generate missing values based on random noises, scMultiGAN employs a two-stage training process and utilizes multiple GANs to achieve cell-specific imputation. Experimental results show the efficacy of scMultiGAN in imputation accuracy, cell clustering, differential gene expression analysis and trajectory analysis, significantly outperforming existing state-of-the-art techniques. Additionally, scMultiGAN is scalable to large scRNA-seq datasets and consistently performs well across sequencing platforms. The scMultiGAN code is freely available at https://github.com/Galaxy8172/scMultiGAN.

Deep spatial-omics analysis of Head & Neck carcinomas provides alternative therapeutic targets and rationale for treatment failure

Immune checkpoint inhibitor (ICI) therapy has had limited success (<30%) in treating metastatic recurrent Head and Neck Oropharyngeal Squamous Cell Carcinomas (OPSCCs). We postulate that spatial determinants in the tumor play a critical role in cancer therapy outcomes. Here, we describe the case of a male patient diagnosed with p16+ OPSCC and extensive lung metastatic disease who failed Nivolumab and Pembrolizumab/Lenvatinib therapies. Using advanced integrative spatial proteogenomic analysis on the patient's recurrent OPSCC tumors we demonstrate that: (i) unbiased tissue clustering based on spatial transcriptomics (ST) successfully detected tumor cells and enabled the investigation of phenotypic traits such as proliferation or drug-resistance genes in the tumor's leading-edge and core; (ii) spatial proteomic imagining used in conjunction with ST (SpiCi, Spatial Proteomics inferred Cell identification) can resolve the profiling of tumor infiltrating immune cells, (iii) ST data allows for the discovery and ranking of clinically relevant alternative medicines based on their interaction with their matching ligand-receptor. Importantly, when the spatial profiles of ICI pre- and post-failure OPSCC tumors were compared, they exhibited highly similar PD-1/PD-L1low and VEGFAhigh expression, suggesting that these new tumors were not the product of ICI resistance but rather of Lenvatinib dose reduction due to complications. Our work establishes a path for incorporating spatial-omics in clinical settings to facilitate treatment personalization.

A Single-Cell Atlas of an Early Mongolian Sheep Embryo

Cell types have been established during organogenesis based on early mouse embryos. However, our understanding of cell types and molecular mechanisms in the early embryo development of Mongolian sheep has been hampered. This study presents the first comprehensive single-cell transcriptomic characterization at E16 in Ujumqin sheep and Hulunbuir short-tailed sheep. Thirteen major cell types were identified at E16 in Ujumqin sheep, and eight major cell types were identified at E16 in Hulunbuir short-tailed sheep. Function enrichment analysis showed that several pathways were significantly enriched in the TGF-beta signaling pathway, the Hippo signaling pathway, the platelet activation pathway, the riboflavin metabolism pathway, the Wnt signaling pathway, regulation of the actin cytoskeleton, and the insulin signaling pathway in the notochord cluster. Glutathione metabolism, glyoxylate, and dicarboxylate metabolism, the citrate cycle, thyroid hormone synthesis, pyruvate metabolism, cysteine and methionine metabolism, thermogenesis, and the VEGF signaling pathway were significantly enriched in the spinal cord cluster. Steroid biosynthesis, riboflavin metabolism, the cell cycle, the Hippo signaling pathway, the Hedgehog signaling pathway, the FoxO signaling pathway, the JAK-STAT signaling pathway, and the Wnt signaling pathway were significantly enriched in the paraxial mesoderm cluster. The notochord cluster, spinal cord cluster, and paraxial mesoderm cluster were found to be highly associated with tail development. Pseudo-time analysis demonstrated that the mesenchyme can translate to the notochord in Ujumqin sheep. Molecular assays revealed that the Hippo signaling pathway was enriched in Ujumqin sheep. This comprehensive single-cell map revealed previously unrecognized signaling pathways that will further our understanding of the mechanism of short-tailed sheep formation.

ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double dipping

In typical single-cell RNA-seq (scRNA-seq) data analysis, a clustering algorithm is applied to find putative cell types as clusters, and then a statistical differential expression (DE) test is employed to identify the differentially expressed (DE) genes between the cell clusters. However, this common procedure uses the same data twice, an issue known as "double dipping": the same data is used twice to define cell clusters as potential cell types and DE genes as potential cell-type marker genes, leading to false-positive cell-type marker genes even when the cell clusters are spurious. To overcome this challenge, we propose ClusterDE, a post-clustering DE method for controlling the false discovery rate (FDR) of identified DE genes regardless of clustering quality, which can work as an add-on to popular pipelines such as Seurat. The core idea of ClusterDE is to generate real-data-based synthetic null data containing only one cluster, as contrast to the real data, for evaluating the whole procedure of clustering followed by a DE test. Using comprehensive simulation and real data analysis, we show that ClusterDE has not only solid FDR control but also the ability to identify cell-type marker genes as top DE genes and distinguish them from housekeeping genes. ClusterDE is fast, transparent, and adaptive to a wide range of clustering algorithms and DE tests. Besides scRNA-seq data, ClusterDE is generally applicable to post-clustering DE analysis, including single-cell multi-omics data analysis.

Single-Cell Transcriptome and Pigment Biochemistry Analysis Reveals the Potential for the High Nutritional and Medicinal Value of Purple Sea Cucumbers

The sea cucumber Apostichopus japonicus has important nutritional and medicinal value. Unfortunately, we know little of the source of active chemicals in this animal, but the plentiful pigments of these animals are thought to function in intriguing ways for translation into clinical and food chemistry usage. Here, we found key cell groups with the gene activity predicted for the color morphology of sea cucumber body using single-cell RNA-seq. We refer to these cell populations as melanocytes and quinocytes, which are responsible for the synthesis of melanin and quinone pigments, respectively. We integrated analysis of pigment biochemistry with the transcript profiles to illuminate the molecular mechanisms regulating distinct pigment formation in echinoderms. In concert with the correlated pigment analysis from each color morph, this study expands our understanding of medically important pigment production, as well as the genetic mechanisms for color morphs, and provides deep datasets for exploring advancements in the fields of bioactives and nutraceuticals.

Single neonatal estrogen implant sterilizes female animals by decreasing hypothalamic KISS1 expression

Reproductive sterilization by surgical gonadectomy is strongly advocated to help manage animal populations, especially domesticated pets, and to prevent reproductive behaviors and diseases. This study explored the use of a single-injection method to induce sterility in female animals as an alternative to surgical ovariohysterectomy. The idea was based on our recent finding that repetitive daily injection of estrogen into neonatal rats disrupted hypothalamic expression of Kisspeptin (KISS1), the neuropeptide that triggers and regulates pulsatile secretion of GnRH. Neonatal female rats were dosed with estradiol benzoate (EB) either by daily injections for 11 days or by subcutaneous implantation of an EB-containing silicone capsule designed to release EB over 2-3 weeks. Rats treated by either method did not exhibit estrous cyclicity, were anovulatory, and became infertile. The EB-treated rats had fewer hypothalamic Kisspeptin neurons, but the GnRH-LH axis remained responsive to Kisspeptin stimulation. Because it would be desirable to use a biodegradable carrier that is also easier to handle, an injectable EB carrier was developed from PLGA microspheres to provide pharmacokinetics comparable to the EB-containing silicone capsule. A single neonatal injection of EB-microspheres at an equivalent dosage resulted in sterility in the female rat. In neonatal female Beagle dogs, implantation of an EB-containing silicone capsule also reduced ovarian follicle development and significantly inhibited KISS1 expression in the hypothalamus. None of the treatments produced any concerning health effects, other than infertility. Therefore, further development of this technology for sterilization in domestic female animals, such as dogs and cats is worthy of investigation.

Targeting the IL1β Pathway for Cancer Immunotherapy Remodels the Tumor Microenvironment and Enhances Antitumor Immune Responses

High levels of IL1β can result in chronic inflammation, which in turn can promote tumor growth and metastasis. Inhibition of IL1β could therefore be a promising therapeutic option in the treatment of cancer. Here, the effects of IL1β blockade induced by the mAbs canakinumab and gevokizumab were evaluated alone or in combination with docetaxel, anti-programmed cell death protein 1 (anti-PD-1), anti-VEGFα, and anti-TGFβ treatment in syngeneic and humanized mouse models of cancers of different origin. Canakinumab and gevokizumab did not show notable efficacy as single-agent therapies; however, IL1β blockade enhanced the effectiveness of docetaxel and anti-PD-1. Accompanying these effects, blockade of IL1β alone or in combination induced significant remodeling of the tumor microenvironment (TME), with decreased numbers of immune suppressive cells and increased tumor infiltration by dendritic cells (DC) and effector T cells. Further investigation revealed that cancer-associated fibroblasts (CAF) were the cell type most affected by treatment with canakinumab or gevokizumab in terms of change in gene expression. IL1β inhibition drove phenotypic changes in CAF populations, particularly those with the ability to influence immune cell recruitment. These results suggest that the observed remodeling of the TME following IL1β blockade may stem from changes in CAF populations. Overall, the results presented here support the potential use of IL1β inhibition in cancer treatment. Further exploration in ongoing clinical studies will help identify the best combination partners for different cancer types, cancer stages, and lines of treatment.

High-resolution anatomical and spatial transcriptome analyses reveal two types of meristematic cell pools within the secondary vascular tissue of poplar stem

The secondary vascular tissue emanating from meristems is central to understanding how vascular plants such as forest trees evolve, grow, and regulate secondary radial growth. However, the overall molecular characterization of meristem origins and developmental trajectories from primary to secondary vascular tissues in woody tree stems is technically challenging. In this study, we combined high-resolution anatomic analysis with a spatial transcriptome (ST) technique to define features of meristematic cells in a developmental gradient from primary to secondary vascular tissues in poplar stems. The tissue-specific gene expression of meristems and derived vascular tissue types were accordingly mapped to specific anatomical domains. Pseudotime analyses were used to track the origins and changes of meristems throughout the development from primary to secondary vascular tissues. Surprisingly, two types of meristematic-like cell pools within secondary vascular tissues were inferred based on high-resolution microscopy combined with ST, and the results were confirmed by in situ hybridization of, transgenic trees, and single-cell sequencing. The rectangle shape procambium-like (PCL) cells develop from procambium meristematic cells and are located within the phloem domain to produce phloem cells, whereas fusiform shape cambium zone (CZ) meristematic cells develop from fusiform metacambium meristematic cells and are located inside the CZ to produce xylem cells. The gene expression atlas and transcriptional networks spanning the primary transition to secondary vascular tissues generated in this work provide new resources for studying the regulation of meristem activities and the evolution of vascular plants. A web server (https://pgx.zju.edu.cn/stRNAPal/) was also established to facilitate the use of ST RNA-seq data.

Isolating peripheral blood mononuclear cells from HIV-infected patients for single-cell RNA sequencing and integration analysis

Single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) allows the dissection of transcriptional changes in immune cells with HIV infection. Here, we isolate PBMCs from HIV-infected individuals. After counting the cell number and verifying cell viability, we perform scRNA-seq for PBMCs on the 10× Genomics protocol and the Illumina NovaSeq 6000 sequencing platform. Furthermore, we analyze the function and cellular trajectories of B cell subsets and B cell receptor (BCR) repertoire after filtering raw sequences data and normalizing gene expression. For complete details on the use and execution of this protocol, please refer to Jia et al. (2022).¹.

Single-cell transcriptomic dissection of the toxic impact of di(2-ethylhexyl) phthalate on immature testicular development at the neonatal stage

Di(2-ethylhexyl) phthalate (DEHP) early exposure leads to immature testicular injury, and we aimed to utilize single-cell RNA (scRNA) sequencing to comprehensively assess the toxic effect of DEHP on testicular development. Therefore, we gavaged pregnant C57BL/6 mice with 750 mg/kg body weight DEHP from gestational day 13.5 to delivery and performed scRNA sequencing of neonatal testes at postnatal day 5.5. The results revealed the gene expression dynamics in testicular cells. DEHP disrupted the developmental trajectory of germ cells and the balance between the self-renewal and differentiation of spermatogonial stem cells. Additionally, DEHP caused an abnormal developmental trajectory, cytoskeletal damage and cell cycle arrest in Sertoli cells; disrupted the metabolism of testosterone in Leydig cells; and disturbed the developmental trajectory in peritubular myoid cells. Elevated oxidative stress and excessive apoptosis mediated by p53 were observed in almost all testicular cells. The intercellular interactions among four cell types were altered, and biological processes related to glial cell line-derived neurotrophic factor (GDNF), transforming growth factor-β (TGF-β), NOTCH, platelet-derived growth factor (PDGF) and WNT signaling pathways were enriched after DEHP treatment. These findings systematically describe the damaging effects of DEHP on the immature testes and provide substantial novel insights into the reproductive toxicity of DEHP.

Matters arising: Insufficient evidence that pancreatic β cells are derived from adult ductal Neurog3-expressing progenitors

Understanding the origin of pancreatic β cells has profound implications for regenerative therapies in diabetes. For over a century, it was widely held that adult pancreatic duct cells act as endocrine progenitors, but lineage-tracing experiments challenged this dogma. Gribben et al. recently used two existing lineage-tracing models and single-cell RNA sequencing to conclude that adult pancreatic ducts contain endocrine progenitors that differentiate to insulin-expressing β cells at a physiologically important rate. We now offer an alternative interpretation of these experiments. Our data indicate that the two Cre lines that were used directly label adult islet somatostatin-producing ∂ cells, which precludes their use to assess whether β cells originate from duct cells. Furthermore, many labeled ∂ cells, which have an elongated neuron-like shape, were likely misclassified as β cells because insulin-somatostatin coimmunolocalizations were not used. We conclude that most evidence so far indicates that endocrine and exocrine lineage borders are rarely crossed in the adult pancreas.

Benchmarking integration of single-cell differential expression

Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. However, strategies to integrate differential expression analysis of single-cell data remain underinvestigated. Here, we benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches. We show that batch effects, sequencing depth and data sparsity substantially impact their performances. Notably, we find that the use of batch-corrected data rarely improves the analysis for sparse data, whereas batch covariate modeling improves the analysis for substantial batch effects. We show that for low depth data, single-cell techniques based on zero-inflation model deteriorate the performance, whereas the analysis of uncorrected data using limmatrend, Wilcoxon test and fixed effects model performs well. We suggest several high-performance methods under different conditions based on various simulation and real data analyses. Additionally, we demonstrate that differential expression analysis for a specific cell type outperforms that of large-scale bulk sample data in prioritizing disease-related genes.

Dynamic cellular and molecular characteristics of spermatogenesis in the viviparous marine teleost Sebastes schlegelii†

Spermatogenesis is a dynamic cell developmental process that is essential for reproductive success. Vertebrates utilize a variety of reproductive strategies, including sperm diversity, and internal and external fertilization. Research on the cellular and molecular dynamic changes involved in viviparous teleost spermatogenesis, however, is currently lacking. Here, we combined cytohistology, 10 × genomic single-cell RNA-seq, and transcriptome technology to determine the dynamic development characteristics of the spermatogenesis of Sebastes schlegelii. The expressions of lhcgr (Luteinizing hormone/Choriogonadotropin receptor), fshr (follicle-stimulating hormone receptor), ar (androgen receptor), pgr (progesterone receptor), and cox (cyclo-oxygen-ase), as well as the prostaglandin E and F levels peaked during the maturation period, indicating that they were important for sperm maturation and mating. Fifteen clusters were identified based on the 10 × genomic single-cell results. The cell markers of the sub-cluster were identified by their upregulation; piwil, dazl, and dmrt1 were upregulated and identified as spermatogonium markers, and sycp1/3 and spo11 were identified as spermatocyte markers. For S. schlegelii, the sperm head nucleus was elongated (spherical to streamlined in shape), which is a typical characteristic for sperm involved in internal fertilization. We also identified a series of crucial genes associated with spermiogenesis, such as spata6, spag16, kif20a, trip10, and klf10, while kif2c, kifap3, fez2, and spaca6 were found to be involved in nucleus elongation. The results of this study will enrich our cellular and molecular knowledge of spermatogenesis and spermiogenesis in fish that undergo internal fertilization.

Single-cell profiling of alveolar rhabdomyosarcoma reveals RAS pathway inhibitors as cell-fate hijackers with therapeutic relevance

Rhabdomyosarcoma (RMS) is a group of pediatric cancers with features of developing skeletal muscle. The cellular hierarchy and mechanisms leading to developmental arrest remain elusive. Here, we combined single-cell RNA sequencing, mass cytometry, and high-content imaging to resolve intratumoral heterogeneity of patient-derived primary RMS cultures. We show that the aggressive alveolar RMS (aRMS) subtype contains plastic muscle stem-like cells and cycling progenitors that drive tumor growth, and a subpopulation of differentiated cells that lost its proliferative potential and correlates with better outcomes. While chemotherapy eliminates cycling progenitors, it enriches aRMS for muscle stem-like cells. We screened for drugs hijacking aRMS toward clinically favorable subpopulations and identified a combination of RAF and MEK inhibitors that potently induces myogenic differentiation and inhibits tumor growth. Overall, our work provides insights into the developmental states underlying aRMS aggressiveness, chemoresistance, and progression and identifies the RAS pathway as a promising therapeutic target.

Variable Selection for Sparse Data with Applications to Vaginal Microbiome and Gene Expression Data

Sparse data with a high portion of zeros arise in various disciplines. Modeling sparse high-dimensional data is a challenging and growing research area. In this paper, we provide statistical methods and tools for analyzing sparse data in a fairly general and complex context. We utilize two real scientific applications as illustrations, including a longitudinal vaginal microbiome data and a high dimensional gene expression data. We recommend zero-inflated model selections and significance tests to identify the time intervals when the pregnant and non-pregnant groups of women are significantly different in terms of Lactobacillus species. We apply the same techniques to select the best 50 genes out of 2426 sparse gene expression data. The classification based on our selected genes achieves 100% prediction accuracy. Furthermore, the first four principal components based on the selected genes can explain as high as 83% of the model variability.

Single cell RNA sequencing analysis of mouse cochlear supporting cell transcriptomes with activated ERBB2 receptor indicates a cell-specific response that promotes CD44 activation

Hearing loss caused by the death of cochlear hair cells (HCs) might be restored through regeneration from supporting cells (SCs) via dedifferentiation and proliferation, as observed in birds. In a previous report, ERBB2 activation in a subset of cochlear SCs promoted widespread down-regulation of SOX2 in neighboring cells, proliferation, and the differentiation of HC-like cells. Here we analyze single cell transcriptomes from neonatal mouse cochlear SCs with activated ERBB2, with the goal of identifying potential secreted effectors. ERBB2 induction in vivo generated a new population of cells with de novo expression of a gene network. Called small integrin-binding ligand n-linked glycoproteins (SIBLINGs), these ligands and their regulators can alter NOTCH signaling and promote cell survival, proliferation, and differentiation in other systems. We validated mRNA expression of network members, and then extended our analysis to older stages. ERBB2 signaling in young adult SCs also promoted protein expression of gene network members. Furthermore, we found proliferating cochlear cell aggregates in the organ of Corti. Our results suggest that ectopic activation of ERBB2 signaling in cochlear SCs can alter the microenvironment, promoting proliferation and cell rearrangements. Together these results suggest a novel mechanism for inducing stem cell-like activity in the adult mammalian cochlea.

Harnessing Single-Cell RNA Sequencing to Identify Dendritic Cell Types, Characterize Their Biological States, and Infer Their Activation Trajectory

Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce the defense mechanisms the best suited to face the threat. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions and how.To decipher the nature, functions, and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single-cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning in the field. In addition, awareness must be raised on the need for specific, robust, and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories, and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on GitHub. We hope that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types and that it will contribute to establishing high standards in the field.