Single-cell RNA sequencing for the study of development, physiology and disease

HPV E6/E7: insights into their regulatory role and mechanism in signaling pathways in HPV-associated tumor

Human papillomavirus (HPV) is a class of envelope-free double-stranded DNA virus. HPV infection has been strongly associated with the development of many malignancies, such as cervical, anal and oral cancers. The viral oncoproteins E6 and E7 perform central roles on HPV-induced carcinogenic processes. During tumor development, it usually goes along with the activation of abnormal signaling pathways. E6 and E7 induces changes in cell cycle, proliferation, invasion, metastasis and other biological behaviors by affecting downstream tumor-related signaling pathways, thus promoting malignant transformation of cells and ultimately leading to tumorigenesis and progression. Here, we summarized that E6 and E7 proteins promote HPV-associated tumorigenesis and development by regulating the activation of various tumor-related signaling pathways, for example, the Wnt/β-catenin, PI3K/Akt, and NF-kB signaling pathway. We also discussed the importance of HPV-encoded E6 and E7 and their regulated tumor-related signaling pathways for the diagnosis and effective treatment of HPV-associated tumors.

The study of intercalated cells using ex vivo techniques: primary cell culture, cell lines, kidney slices, and organoids

This review summarizes methods to study kidney intercalated cell (IC) function ex vivo. While important for acid-base homeostasis, IC dysfunction is often not recognized clinically until it becomes severe. The advantage of using ex vivo techniques is that they allow for the differential evaluation of IC function in controlled environments. Although in vitro kidney tubular perfusion is a classical ex vivo technique to study IC, here we concentrate on primary cell cultures, immortalized cell lines, and ex vivo kidney slices. Ex vivo techniques are useful in evaluating IC signaling pathways that allow rapid responses to extracellular changes in pH, CO2, and bicarbonate (HCO3-). However, these methods for IC work can also be challenging, as cell lines that recapitulate IC do not proliferate easily in culture. Moreover, a "pure" IC population in culture does not necessarily replicate its collecting duct (CD) environment, where ICs are surrounded by the more abundant principal cells (PCs). It is reassuring that many findings obtained in ex vivo IC systems signaling have been largely confirmed in vivo. Some of these newly identified signaling pathways reveal that ICs are important for regulating NaCl reabsorption, thus suggesting new frontiers to target antihypertensive treatments. Moreover, recent single-cell characterization studies of kidney epithelial cells revealed a dual developmental origin of IC, as well as the presence of novel CD cell types with certain IC characteristics. These exciting findings present new opportunities for the study of IC ex vivo and will likely rediscover the importance of available tools in this field.NEW & NOTEWORTHY The study of kidney intercalated cells has been limited by current cell culture and kidney tissue isolation techniques. This review is to be used as a reference to select ex vivo techniques to study intercalated cells. We focused on the use of cell lines and kidney slices as potential useful models to study membrane transport proteins. We also review how novel collecting duct organoids may help better elucidate the role of these intriguing cells.

Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity

Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.

scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data

BACKGROUND

The rapid emergence of single-cell RNA-seq (scRNA-seq) data presents remarkable opportunities for broad investigations through integration analyses. However, most integration models are black boxes that lack interpretability or are hard to train.

RESULTS

To address the above issues, we propose scInterpreter, a deep learning-based interpretable model. scInterpreter substantially outperforms other state-of-the-art (SOTA) models in multiple benchmark datasets. In addition, scInterpreter is extensible and can integrate and annotate atlas scRNA-seq data. We evaluated the robustness of scInterpreter in a variety of situations. Through comparison experiments, we found that with a knowledge prior, the training process can be significantly accelerated. Finally, we conducted interpretability analysis for each dimension (pathway) of cell representation in the embedding space.

CONCLUSIONS

The results showed that the cell representations obtained by scInterpreter are full of biological significance. Through weight sorting, we found several new genes related to pathways in PBMC dataset. In general, scInterpreter is an effective and interpretable integration tool. It is expected that scInterpreter will bring great convenience to the study of single-cell transcriptomics.

scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data

Recent developments in single-cell technology have enabled the exploration of cellular heterogeneity at an unprecedented level, providing invaluable insights into various fields, including medicine and disease research. Cell type annotation is an essential step in its omics research. The mainstream approach is to utilize well-annotated single-cell data to supervised learning for cell type annotation of new singlecell data. However, existing methods lack good generalization and robustness in cell annotation tasks, partially due to difficulties in dealing with technical differences between datasets, as well as not considering the heterogeneous associations of genes in regulatory mechanism levels. Here, we propose the scPML model, which utilizes various gene signaling pathway data to partition the genetic features of cells, thus characterizing different interaction maps between cells. Extensive experiments demonstrate that scPML performs better in cell type annotation and detection of unknown cell types from different species, platforms, and tissues.

Microarray Analysis for Transcriptomic Profiling of Myocardium in Patients with Fatal Myocardial Infarction

Transcriptomic evidence from human myocardium in myocardial infarction (MI) is still not sufficient. Thus, there is a need for studies on human cardiac samples in relation to the clinical data of patients. The purpose of our pilot study was to investigate the transcriptomic profile of myocardium in the infarct zone, in comparison to the remote myocardium, in patients with fatal MI, via microarray analysis. This study included four patients with fatal MI type 1. We selected histologically verified samples from within the infarct area (n = 4) and remote myocardium (n = 4). The whole transcriptome was evaluated using microarray analysis. Differentially expressed genes (DEGs) clustered in the infarct area and in the remote myocardium allowed their differentiation. We identified a total of 1785 DEGs (8.32%) in the infarct area, including 1692 up-regulated (94.79%) and 93 down-regulated (5.21%) genes. The top 10 up-regulated genes were TRAIL, SUCLA2, NAE1, PDCL3, OSBPL5, FCGR2C, SELE, CEP63, ST3GAL3 and C4orf3. In the infarct area, we found up-regulation of seventeen apoptosis-related genes, eleven necroptosis-related, and six necrosis-related genes. Transcriptome profiling of the myocardium in patients with MI remains a relevant area of research for the formation of new scientific hypotheses and a potential way to increase the translational significance of studies into myocardial infarction.

Single-cell sequencing in pancreatic cancer research: A deeper understanding of heterogeneity and therapy

Pancreatic cancer, including pancreatic ductal adenocarcinomas (PDACs), is a malignant tumor with characteristics of tumor-stroma interactions. Patients often have a poor prognosis and a poor long-term survival rate. In recent years, rapidly-developing single-cell sequencing techniques have been used to analyze cell populations at a single-cell resolution, so that it is now possible to have a more in-depth and clearer understanding of the genetic composition of pancreatic cancer. In this review, we provide an overview of the current single-cell sequencing techniques and their applications in the exploration of intratumoral heterogeneity, the tumor microenvironment, therapy resistance, and novel treatments. Our hope is to provide new insight into the potential of precision therapy, which will perhaps one day lead to significant advances in PDAC treatment.

Enhanced oxidative phosphorylation, re-organized intracellular signaling, and epigenetic de-silencing as revealed by oligodendrocyte translatome analysis after contusive spinal cord injury

Reducing the loss of oligodendrocytes (OLs) is a major goal for neuroprotection after spinal cord injury (SCI). Therefore, the OL translatome was determined in Ribotag:Plp1-CreERT2 mice at 2, 10, and 42 days after moderate contusive T9 SCI. At 2 and 42 days, mitochondrial respiration- or actin cytoskeleton/cell junction/cell adhesion mRNAs were upregulated or downregulated, respectively. The latter effect suggests myelin sheath loss/morphological simplification which is consistent with downregulation of cholesterol biosynthesis transcripts on days 10 and 42. Various regulators of pro-survival-, cell death-, and/or oxidative stress response pathways showed peak expression acutely, on day 2. Many acutely upregulated OL genes are part of the repressive SUZ12/PRC2 operon suggesting that epigenetic de-silencing contributes to SCI effects on OL gene expression. Acute OL upregulation of the iron oxidoreductase Steap3 was confirmed at the protein level and replicated in cultured OLs treated with the mitochondrial uncoupler FCCP. Hence, STEAP3 upregulation may mark mitochondrial dysfunction. Taken together, in SCI-challenged OLs, acute and subchronic enhancement of mitochondrial respiration may be driven by axonal loss and subsequent myelin sheath degeneration. Acutely, the OL switch to oxidative phosphorylation may lead to oxidative stress that is further amplified by upregulation of such enzymes as STEAP3.

scFED: Clustering Identifying Cell Types of scRNA-Seq Data Based on Feature Engineering Denoising

Recently developed single-cell RNA-seq (scRNA-seq) technology has given researchers the chance to investigate single-cell level of disease development. Clustering is one of the most essential strategies for analyzing scRNA-seq data. Choosing high-quality feature sets can significantly enhance the outcomes of single-cell clustering and classification. But computationally burdensome and highly expressed genes cannot afford a stabilized and predictive feature set for technical reasons. In this study, we introduce scFED, a feature-engineered gene selection framework. scFED identifies prospective feature sets to eliminate the noise fluctuation. And fuse them with existing knowledge from the tissue-specific cellular taxonomy reference database (CellMatch) to avoid the influence of subjective factors. Then present a reconstruction approach for noise reduction and crucial information amplification. We apply scFED on four genuine single-cell datasets and compare it with other techniques. According to the results, scFED improves clustering, decreases dimension of the scRNA-seq data, improves cell type identification when combined with clustering algorithms, and has higher performance than other methods. Therefore, scFED offers certain benefits in scRNA-seq data gene selection.

Paired single-cell multi-omics data integration with Mowgli

The profiling of multiple molecular layers from the same set of cells has recently become possible. There is thus a growing need for multi-view learning methods able to jointly analyze these data. We here present Multi-Omics Wasserstein inteGrative anaLysIs (Mowgli), a novel method for the integration of paired multi-omics data with any type and number of omics. Of note, Mowgli combines integrative Nonnegative Matrix Factorization and Optimal Transport, enhancing at the same time the clustering performance and interpretability of integrative Nonnegative Matrix Factorization. We apply Mowgli to multiple paired single-cell multi-omics data profiled with 10X Multiome, CITE-seq, and TEA-seq. Our in-depth benchmark demonstrates that Mowgli's performance is competitive with the state-of-the-art in cell clustering and superior to the state-of-the-art once considering biological interpretability. Mowgli is implemented as a Python package seamlessly integrated within the scverse ecosystem and it is available at http://github.com/cantinilab/mowgli .

Single-cell dynamics of liver development in postnatal pigs

The postnatal development of the liver, an essential organ for metabolism and immunity, remains poorly characterized at the single-cell resolution. Here, we generated single-nucleus and single-cell transcriptomes of 84,824 pig liver cells at four postnatal time points: day 30, 42, 150, and 730. We uncovered 23 cell types, including three rare cell types: plasmacytoid dendritic cells, CAVIN3+IGF2+ endothelial cells, and EBF1+ fibroblasts. The latter two were verified by multiplex immunohistochemistry. Trajectory and gene regulatory analyses revealed 33 genes that encode transcription factors associated with hepatocyte development and function, including NFIL3 involved in regulating hepatic metabolism. We characterized the spatiotemporal heterogeneity of liver endothelial cells, identified and validated leucine zipper protein 2 (LUZP2) as a novel adult liver sinusoidal endothelial cell-specific transcription factor. Lymphoid cells (NK and T cells) governed the immune system of the pig liver since day 30. Furthermore, we identified a cluster of tissue-resident NK cells, which displayed virus defense functions, maintained proliferative features at day 730, and manifested a higher conservative transcription factor expression pattern in humans than in mouse liver. Our study presents the most comprehensive postnatal liver development single-cell atlas and demonstrates the metabolic and immune changes across the four age stages.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the heterogeneity of cancer-associated fibroblasts in TNBC

BACKGROUND

The treatment of triple-negative breast cancer (TNBC) is one of the main focuses and key difficulties because of its heterogeneity, and the source of this heterogeneity is unclear.

METHODS

Single-cell RNA (scRNA) and transcriptomics data of TNBC and normal breast samples were retrieved from Gene Expression Omnibus (GEO) database and TCGA-BRCA database. These cells were clustered using the t-SNE and UMAP method, and the marker genes for each cluster were found. We annotated the clusters using the published literature, CellMarker database and "SingleR" R package.

RESULTS

A total of 1535 cells and 21785 genes from 6 TNBC patients and 2068 cells and 15868 genes from 3 normal breast tissues were used for downstream analyses. The scRNA data were divided into 14 clusters labeled into 8 cell types, including epithelial cells, immunocytes, CAFs/fibroblasts and etc. In the TNBC samples, CAFs were divided into three clusters and labelled as prCAFs, myCAFs and emCAFs, and the marker genes were DCN, FAP and RGS5, respectively. The prCAF subgroup is functionally characterized by promoting proliferation and multi drug resistance; myCAF subgroup is involved in constituting the extracellular matrix and collagen production, matrix composition and collagen production, and the emCAF functionally characterized by energy metabolism.

CONCLUSIONS

TNBC has inter- and intra-tumor heterogeneity, and CAF is one of the sources of this heterogeneity. CD74, SASH3, CD2, TAGAP and CCR7 served as significant marker genes with prognostic and therapeutic value.

Particle sorting method based on swirl induction

Fluid-based methods for particle sorting demonstrate increasing appeal in many areas of biosciences due to their biocompatibility and cost-effectiveness. Herein, we construct a microfluidic sorting system based on a swirl microchip. The impact of microchannel velocity on the swirl stagnation point as well as particle movement is analyzed through simulation and experiment. Moreover, the quantitative mapping relationship between flow velocity and particle position distribution is established. With this foundation established, a particle sorting method based on swirl induction is proposed. Initially, the particle is captured by a swirl. Then, the Sorting Region into which the particle aims to enter is determined according to the sorting condition and particle characteristic. Subsequently, the velocities of the microchannels are adjusted to control the swirl, which will induce the particle to enter its corresponding Induction Region. Thereafter, the velocities are adjusted again to change the fluid field and drive the particle into a predetermined Sorting Region, hence the sorting is accomplished. We have extensively conducted experiments taking particle size or color as a sorting condition. An outstanding sorting success rate of 98.75% is achieved when dealing with particles within the size range of tens to hundreds of micrometers in radius, which certifies the effectiveness of the proposed sorting method. Compared to the existing sorting techniques, the proposed method offers greater flexibility. The adjustment of sorting conditions or particle parameters no longer requires complex chip redesign, because such sorting tasks can be successfully realized through simple microchannel velocities control.

Integrative Analysis of Single-Cell and Bulk RNA Sequencing Reveals Prognostic Characteristics of Macrophage Polarization-Related Genes in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a group of cancers with poor prognosis. The combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) can identify important genes involved in cancer development and progression from a broader perspective.

Methods

The scRNA-seq data and bulk RNA-seq data of LUAD were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Analyzing scRNA-seq for core cells in the GSE131907 dataset, and the uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Macrophage polarization-associated subtypes were acquired from the TCGA-LUAD dataset after analysis, followed by further identification of differentially expressed genes (DEGs) in the TCGA-LUAD dataset (normal/LUAD tissue samples, two subtypes). Venn diagrams were utilized to visualize differentially expressed and highly variable macrophage polarization-related genes. Subsequently, a prognostic risk model for LUAD patients was constructed by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO), and the model was investigated for stability in the external data GSE72094. After analyzing the correlation between the trait genes and significantly mutated genes, the immune infiltration between the high/low-risk groups was then examined. The Monocle package was applied to analyze the pseudo-temporal trajectory analysis of different cell clusters in macrophage clusters. Subsequently, cell clusters of data macrophages were selected as key cell clusters to explore the role of characteristic genes in different cell populations and to identify transcription factors (TFs) that affect signature genes. Finally, qPCR were employed to validate the expression levels of prognosis signature genes in LUAD.

Results

424 macrophage highly variable genes, 3920 DEGs, and 9561 DEGs were obtained from macrophage clusters, the macrophage polarization-related subtypes, and normal/LUAD tissue samples, respectively. Twenty-eight differentially expressed and highly mutated MPRGs were obtained. A prognostic risk model with 7 DE-MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) was constructed. This prognostic model still has a good prediction effect in the GSE72094 dataset. ZNF536 and DNAH9 were mutated in the low-risk group, while COL11A1 was mutated in the high-risk group, and they were highly correlated with the characteristic genes. A total of 11 immune cells were significantly different in the high/low-risk groups. Five cell types were again identified in the macrophage cluster, and then NK cells: CD56hiCD62L+ differentiated earlier and were present mainly on 2 branches. While macrophages were present on 2 branches and differentiated later. It was found that the expression levels of BCLAF1 and MAX were higher in cluster 1, which might be the TFs affecting the expression of the characteristic genes. Moreover, qPCR confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis.

Conclusion

Seven MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) were identified as prognostic genes for LUAD and revealed the mechanisms of MPRGs at the single-cell level.

Comprehensive analyses for the coagulation and macrophage-related genes to reveal their joint roles in the prognosis and immunotherapy of lung adenocarcinoma patients

Purpose

This study aims to explore novel biomarkers related to the coagulation process and tumor-associated macrophage (TAM) infiltration in lung adenocarcinoma (LUAD).

Methods

The macrophage M2-related genes were obtained by Weighted Gene Co-expression Network Analysis (WGCNA) in bulk RNA-seq data, while the TAM marker genes were identified by analyzing the scRNA-seq data, and the coagulation-associated genes were obtained from MSigDB and KEGG databases. Survival analysis was performed for the intersectional genes. A risk score model was subsequently constructed based on the survival-related genes for prognosis prediction and validated in external datasets.

Results

In total, 33 coagulation and macrophage-related (COMAR) genes were obtained, 19 of which were selected for the risk score model construction. Finally, 10 survival-associated genes (APOE, ARRB2, C1QB, F13A1, FCGR2A, FYN, ITGB2, MMP9, OLR1, and VSIG4) were involved in the COMAR risk score model. According to the risk score, patients were equally divided into low- and high-risk groups, and the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. The ROC curve indicated that the risk score model had high sensitivity and specificity, which was validated in multiple external datasets. Moreover, the model also had high efficacy in predicting the clinical outcomes of LUAD patients who received anti-PD-1/PD-L1 immunotherapy.

Conclusion

The COMAR risk score model constructed in this study has excellent predictive value for the prognosis and immunotherapeutic clinical outcomes of patients with LUAD, which provides potential biomarkers for the treatment and prognostic prediction.

A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multi-region gene expression sequencing

Intratumor heterogeneity (ITH) of tumor-infiltrated leukocytes (TILs) is an important phenomenon of cancer biology with potentially profound clinical impacts. Multi-region gene expression sequencing data provide a promising opportunity that allows for explorations of TILs and their intratumor heterogeneity for each subject. Although several existing methods are available to infer the proportions of TILs, considerable methodological gaps exist for evaluating intratumor heterogeneity of TILs with multi-region gene expression data. Here, we develop ICeITH, immune cell estimation reveals intratumor heterogeneity, a Bayesian hierarchical model that borrows cell type profiles as prior knowledge to decompose mixed bulk data while accounting for the within-subject correlations among tumor samples. ICeITH quantifies intratumor heterogeneity by the variability of targeted cellular compositions. Through extensive simulation studies, we demonstrate that ICeITH is more accurate in measuring relative cellular abundance and evaluating intratumor heterogeneity compared with existing methods. We also assess the ability of ICeITH to stratify patients by their intratumor heterogeneity score and associate the estimations with the survival outcomes. Finally, we apply ICeITH to two multi-region gene expression datasets from lung cancer studies to classify patients into different risk groups according to the ITH estimations of targeted TILs that shape either pro- or anti-tumor processes. In conclusion, ICeITH is a useful tool to evaluate intratumor heterogeneity of TILs from multi-region gene expression data.

Plant long non-coding RNAs: identification and analysis to unveil their physiological functions

Eukaryotic genomes encode thousands of RNA molecules; however, only a minimal fraction is translated into proteins. Among the non-coding elements, long non-coding RNAs (lncRNAs) play important roles in diverse biological processes. LncRNAs are associated mainly with the regulation of the expression of the genome; nonetheless, their study has just scratched the surface. This is somewhat due to the lack of widespread conservation at the sequence level, in addition to their relatively low and highly tissue-specific expression patterns, which makes their exploration challenging, especially in plant genomes where only a few of these molecules have been described completely. Recently published high-quality genomes of crop plants, along with new computational tools, are considered promising resources for studying these molecules in plants. This review briefly summarizes the characteristics of plant lncRNAs, their presence and conservation, the different protocols to find these elements, and the limitations of these protocols. Likewise, it describes their roles in different plant physiological phenomena. We believe that the study of lncRNAs can help to design strategies to reduce the negative effect of biotic and abiotic stresses on the yield of crop plants and, in the future, help create fruits and vegetables with improved nutritional content, higher amounts of compounds with positive effects on human health, better organoleptic characteristics, and fruits with a longer postharvest shelf life.

scIBD: a self-supervised iterative-optimizing model for boosting the detection of heterotypic doublets in single-cell chromatin accessibility data

Application of the widely used droplet-based microfluidic technologies in single-cell sequencing often yields doublets, introducing bias to downstream analyses. Especially, doublet-detection methods for single-cell chromatin accessibility sequencing (scCAS) data have multiple assay-specific challenges. Therefore, we propose scIBD, a self-supervised iterative-optimizing model for boosting heterotypic doublet detection in scCAS data. scIBD introduces an adaptive strategy to simulate high-confident heterotypic doublets and self-supervise for doublet-detection in an iteratively optimizing manner. Comprehensive benchmarking on various simulated and real datasets demonstrates the outperformance and robustness of scIBD. Moreover, the downstream biological analyses suggest the efficacy of doublet-removal by scIBD.

Single-Cell Transcriptomics Reveals Cellular Heterogeneity and Complex Cell-Cell Communication Networks in the Mouse Cornea

Purpose

To generate a single-cell RNA-sequencing (scRNA-seq) map and construct cell-cell communication networks of mouse corneas.

Methods

C57BL/6 mouse corneas were dissociated to single cells and subjected to scRNA-seq. Cell populations were clustered and annotated for bioinformatic analysis using the R package "Seurat." Differential expression patterns were validated and spatially mapped with whole-mount immunofluorescence staining. Global intercellular signaling networks were constructed using CellChat.

Results

Unbiased clustering of scRNA-seq transcriptomes of 14,732 cells from 40 corneas revealed 17 cell clusters of six major cell types: nine epithelial cell, three keratocyte, two corneal endothelial cell, and one each of immune cell, vascular endothelial cell, and fibroblast clusters. The nine epithelial cell subtypes included quiescent limbal stem cells, transit-amplifying cells, and differentiated cells from corneas and two minor conjunctival epithelial clusters. CellChat analysis provided an atlas of the complex intercellular signaling communications among all cell types.

Conclusions

We constructed a complete single-cell transcriptomic map and the complex signaling cross-talk among all cell types of the cornea, which can be used as a foundation atlas for further research on the cornea. This study also deepens the understanding of the cellular heterogeneity and heterotypic cell-cell interaction within corneas.

Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.

Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively.

A comprehensive assessment of hurdle and zero-inflated models for single cell RNA-sequencing analysis

Single cell RNA-sequencing (scRNA-seq) technology has significantly advanced the understanding of transcriptomic signatures. Although various statistical models have been used to describe the distribution of gene expression across cells, a comprehensive assessment of the different models is missing. Moreover, the growing number of features associated with scRNA-seq datasets creates new challenges for analytical accuracy and computing speed. Here, we developed a Python-based package (TensorZINB) to solve the zero-inflated negative binomial (ZINB) model using the TensorFlow deep learning framework. We used a sequential initialization method to solve the numerical stability issues associated with hurdle and zero-inflated models. A recursive feature selection protocol was used to optimize feature selections for data processing and downstream differentially expressed gene (DEG) analysis. We proposed a class of hybrid models combining nested models to further improve the model's performance. Additionally, we developed a new method to convert a continuous distribution to its equivalent discrete form, so that statistical models can be fairly compared. Finally, we showed that the proposed TensorFlow algorithm (TensorZINB) was numerically stable and that its computing speed and performance were superior to those of existing ZINB solvers. Moreover, we implemented seven hurdle and zero-inflated statistical models in Python and systematically assessed their performance using a real scRNA-seq dataset. We demonstrated that the ZINB model achieved the lowest Akaike information criterion compared with other models tested. Taken together, TensorZINB was accurate, efficient and scalable for the implementation of ZINB and for large-scale scRNA-seq data analysis with DEG identification.

Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM

BACKGROUND

Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies.

RESULTS

We propose a modification to MuSiC entitled 'DeTREM' which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data.

CONCLUSION

DeTREM improves the deconvolution accuracy of MuSiC and outperforms other deconvolution methods when applied to snRNA-seq data. DeTREM enables accurate cell-type deconvolution in situations where scRNA-seq data are not available. This modification improves characterization cell-type specific effects in brain tissue and identification of cell-type abundance differences under various conditions.

CellAnn: a comprehensive, super-fast, and user-friendly single-cell annotation web server

MOTIVATION

Single-cell sequencing technology has become a routine in studying many biological problems. A core step of analyzing single-cell data is the assignment of cell clusters to specific cell types. Reference-based methods are proposed for predicting cell types for single-cell clusters. However, the scalability and lack of preprocessed reference datasets prevent them from being practical and easy to use.

RESULTS

Here, we introduce a reference-based cell annotation web server, CellAnn, which is super-fast and easy to use. CellAnn contains a comprehensive reference database with 204 human and 191 mouse single-cell datasets. These reference datasets cover 32 organs. Furthermore, we developed a cluster-to-cluster alignment method to transfer cell labels from the reference to the query datasets, which is superior to the existing methods with higher accuracy and higher scalability. Finally, CellAnn is an online tool that integrates all the procedures in cell annotation, including reference searching, transferring cell labels, visualizing results, and harmonizing cell annotation labels. Through the user-friendly interface, users can identify the best annotation by cross-validating with multiple reference datasets. We believe that CellAnn can greatly facilitate single-cell sequencing data analysis.

AVAILABILITY AND IMPLEMENTATION

The web server is available at www.cellann.io, and the source code is available at https://github.com/Pinlyu3/CellAnn_shinyapp.

Single-cell transcriptomics implicates the FEZ1-DKK1 axis in the regulation of corneal epithelial cell proliferation and senescence

Limbal stem/progenitor cells (LSC) represent the source of corneal epithelium renewal. LSC proliferation and differentiation are essential for corneal homeostasis, however, the regulatory mechanism remains largely unexplored. Here, we performed single-cell RNA sequencing and discovered proliferation heterogeneity as well as spontaneously differentiated and senescent cell subgroups in multiply passaged primary LSC. Fasciculation and elongation protein zeta 1 (FEZ1) and Dickkopf-1 (DKK1) were identified as two significant regulators of LSC proliferation and senescence. These two factors were mainly expressed in undifferentiated corneal epithelial cells (CECs). Knocking down the expression of either FEZ1 or DKK1 reduced cell division and caused cell cycle arrest. We observed that DKK1 acted as a downstream target of FEZ1 in LSC and that exogenous DKK1 protein partially prevented growth arrest and senescence upon FEZ1 suppression in vitro. In a mouse model of corneal injury, DKK1 also rescued the corneal epithelium after recovery was inhibited by FEZ1 suppression. Hence, the FEZ1-DKK1 axis was required for CEC proliferation and the juvenile state and can potentially be targeted as a therapeutic strategy for promoting recovery after corneal injury.

Single-cell RNA sequencing analysis of the embryogenic callus clarifies the spatiotemporal developmental trajectories of the early somatic embryo in Dimocarpus longan

Plant embryogenic calli (ECs) can undergo somatic embryogenesis to regenerate plants. This process is mediated by regulatory factors, such as transcription factors and specifically expressed genes, but the precise molecular mechanisms underlying somatic embryogenesis at the single-cell level remain unclear. In this study, we performed high-resolution single-cell RNA sequencing analysis to determine the cellular changes in the EC of the woody plant species Dimocarpus longan (longan) and clarify the continuous cell differentiation trajectories at the transcriptome level. The highly heterogeneous cells in the EC were divided into 12 putative clusters (e.g., proliferating, meristematic, vascular, and epidermal cell clusters). We determined cluster-enriched expression marker genes and found that overexpression of the epidermal cell marker gene GDSL ESTERASE/LIPASE-1 inhibited the hydrolysis of triacylglycerol. In addition, the stability of autophagy was critical for the somatic embryogenesis of longan. The pseudo-timeline analysis elucidated the continuous cell differentiation trajectories from early embryonic cell division to vascular and epidermal cell differentiation during the somatic embryogenesis of longan. Moreover, key transcriptional regulators associated with cell fates were revealed. We found that ETHYLENE RESPONSIVE FACTOR 6 was characterized as a heat-sensitive factor that negatively regulates longan somatic embryogenesis under high-temperature stress conditions. The results of this study provide new spatiotemporal insights into cell division and differentiation during longan somatic embryogenesis at single-cell resolution.

Integrated immunological analysis of single-cell and bulky tissue transcriptomes reveals the role of interactions between M0 macrophages and naïve CD4+ T cells in the immunosuppressive microenvironment of cervical cancer

In recent decades, the incidence and mortality of cervical cancer have declined in developed countries due to the implementation of screening and vaccination programs. However, cervical cancer remains one of the major culprits of cancer-related deaths in young women. Current studies have found that immune cell-related intercellular communication in the tumor microenvironment has a large impact on the construction of the immunosuppressive microenvironment. In this study, we performed a comprehensive immune analysis on bulk RNA-seq and scRNA-seq data obtained from cervical cancer and revealed that two highly plastic cell populations, M0 macrophages and naïve CD4+ T cells, were significantly correlated with prognosis and clinical phenotypes. Notably, signaling between M0 macrophages and naïve CD4+ T cells as well as intracellular transcription factor activity were significantly altered in the tumor state. Furthermore, we identified overlapping genes between the transcription factor target genes of M0 macrophages or naïve CD4+ T cells and the differentially expressed genes in each type of cell, and these overlapping genes were subsequently subjected to an analysis using the LASSO regression model. Finally, we generated a score index that was significantly associated with the clinical prognosis of cervical cancer. In conclusion, interventions to improve the communication between M0 macrophages and naïve CD4+ T cells may help to improve the immunosuppressive microenvironment of cervical cancer and prevent immune evasion. The relevant molecular mechanisms need to be further validated by experimental and cohort studies.

Single-cell analysis of human prepuce reveals dynamic changes in gene regulation and cellular communications

BACKGROUND

The cellular and molecular dynamics of human prepuce are crucial for understanding its biological and physiological functions, as well as the prevention of related genital diseases. However, the cellular compositions and heterogeneity of human prepuce at single-cell resolution are still largely unknown. Here we systematically dissected the prepuce of children and adults based on the single-cell RNA-seq data of 90,770 qualified cells.

RESULTS

We identified 15 prepuce cell subtypes, including fibroblast, smooth muscle cells, T/natural killer cells, macrophages, vascular endothelial cells, and dendritic cells. The proportions of these cell types varied among different individuals as well as between children and adults. Moreover, we detected cell-type-specific gene regulatory networks (GRNs), which could contribute to the unique functions of related cell types. The GRNs were also highly dynamic between the prepuce cells of children and adults. Our cell-cell communication network analysis among different cell types revealed a set of child-specific (e.g., CD96, EPO, IFN-1, and WNT signaling pathways) and adult-specific (e.g., BMP10, NEGR, ncWNT, and NPR1 signaling pathways) signaling pathways. The variations of GRNs and cellular communications could be closely associated with prepuce development in children and prepuce maintenance in adults.

CONCLUSIONS

Collectively, we systematically analyzed the cellular variations and molecular changes of the human prepuce at single-cell resolution. Our results gained insights into the heterogeneity of prepuce cells and shed light on the underlying molecular mechanisms of prepuce development and maintenance.

Single-cell RNA sequencing in dissecting microenvironment of age-related macular degeneration: Challenges and perspectives

Age-related macular degeneration (AMD) is the leading cause of blindness in individuals over the age of 50 years, yet its etiology and pathogenesis largely remain uncovered. Single-cell RNA sequencing (scRNA-seq) technologies are recently developed and have a number of advantages over conventional bulk RNA sequencing techniques in uncovering the heterogeneity of complex microenvironments containing numerous cell types and cell communications during various biological processes. In this review, we summarize the latest discovered cellular components and regulatory mechanisms during AMD development revealed by scRNA-seq. In addition, we discuss the main challenges and future directions in exploring the pathophysiology of AMD equipped with single-cell technologies. Our review underscores the importance of multimodal single-cell platforms (such as single-cell spatiotemporal multi-omics and single-cell exosome omics) as new approaches for basic and clinical AMD research in identifying biomarker, characterizing cellular responses to drug treatment and environmental stimulation.

Transcriptional and open chromatin analysis of bovine skeletal muscle development by single-cell sequencing

Skeletal muscle is a complex heterogeneous tissue and characterizing its cellular heterogeneity and transcriptional and epigenetic signatures are important for understanding the details of its ontogeny. In our study, we applied scRNA-seq and scATAC-seq to investigate the cell types, molecular features, transcriptional and epigenetic regulation, and patterns of developing bovine skeletal muscle from gestational, lactational and adult stages. Detailed molecular analyses were used to dissect cellular heterogeneity, and we deduced the differentiation trajectory of myogenic cells and uncovered their dynamic gene expression profiles. SCENIC analysis was performed to demonstrate key regulons during cell fate decisions. We explored the future expression states of these heterogeneous cells by RNA velocity analysis and found extensive networks of intercellular communication using the toolkit CellChat. Moreover, the transcriptomic and chromatin accessibility modalities were confirmed to be highly concordant, and integrative analysis of chromatin accessibility and gene expression revealed key transcriptional regulators acting during myogenesis. In bovine skeletal muscle, by scRNA-seq and scATAC-seq analysis, different cell types such as adipocytes, endothelial cells, fibroblasts, lymphocytes, monocytes, pericyte cells and eight skeletal myogenic subpopulations were identified at the three developmental stages. The pseudotime trajectory exhibited a distinct sequential ordering for these myogenic subpopulations and eight distinct gene clusters were observed according to their expression pattern. Moreover, specifically expressed TFs (such as MSC, MYF5, MYOD1, FOXP3, ESRRA, BACH1, SIX2 and ATF4) associated with muscle development were predicted, and likely future transcriptional states of individual cells and the developmental dynamics of differentiation among neighbouring cells were predicted. CellChat analysis on the scRNA-seq data set then classified many ligand-receptor pairs among these cell clusters, which were further categorized into significant signalling pathways, including BMP, IGF, WNT, MSTN, ANGPTL, TGFB, TNF, VEGF and FGF. Finally, scRNA-seq and scATAC-seq results were successfully integrated to reveal a series of specifically expressed TFs that are likely to be candidates for the promotion of cell fate transition during bovine skeletal muscle development. Overall, our results outline a single-cell dynamic chromatin/transcriptional landscape for normal bovine skeletal muscle development; these provide an important resource for understanding the structure and function of mammalian skeletal muscle, which will promote research into its biology.

Advancement of single-cell sequencing for clinical diagnosis and treatment of pancreatic cancer

Single-cell sequencing is a high-throughput technique that enables detection of genomic, transcriptomic, and epigenomic information at the individual cell level, offering significant advantages in detecting cellular heterogeneity, precise cell classification, and identifying rare subpopulations. The technique holds tremendous potential in improving the diagnosis and treatment of pancreatic cancer. Moreover, single-cell sequencing provides unique insights into the mechanisms of pancreatic cancer metastasis and cachexia, paving the way for developing novel preventive strategies. Overall, single-cell sequencing has immense potential in promoting early diagnosis, guiding personalized treatment, and preventing complications of pancreatic cancer. Emerging single-cell sequencing technologies will undoubtedly enhance our understanding of the complex biology of pancreatic cancer and pave the way for new directions in its clinical diagnosis and treatment.

ANGPTL2+cancer-associated fibroblasts and SPP1+macrophages are metastasis accelerators of colorectal cancer

Background

Liver metastasis (LM) is a leading cause of cancer-related deaths in CRC patients, whereas the associated mechanisms have not yet been fully elucidated. Therefore, it is urgently needed to deeply explore novel metastasis accelerators and therapeutic targets of LM-CRC.

Methods

The bulk RNA sequencing data and clinicopathological information of CRC patients were enrolled from the TCGA and GEO databases. The single-cell RNA sequencing (scRNA-seq) datasets of CRC were collected from and analyzed in the Tumor Immune Single-cell Hub (TISCH) database. The infiltration levels of cancer-associated fibroblasts (CAFs) and macrophages in CRC tissues were estimated by multiple immune deconvolution algorithms. The prognostic values of genes were identified by the Kaplan-Meier curve with a log-rank test. GSEA analysis was carried out to annotate the significantly enriched gene sets. The biological functions of cells were experimentally verified.

Results

In the present study, hundreds of differentially expressed genes (DEGs) were selected in LM-CRC compared to primary CRC, and these DEGs were significantly associated with the regulation of endopeptidase activity, blood coagulation, and metabolic processes. Then, SPP1, CAV1, ANGPTL2, and COLEC11 were identified as the characteristic DEGs of LM-CRC, and higher expression levels of SPP1 and ANGPTL2 were significantly associated with worse clinical outcomes of CRC patients. In addition, ANGPTL2 and SPP1 mainly distributed in the tumor microenvironment (TME) of CRC tissues. Subsequent scRNA-seq analysis demonstrated that ANGPTL2 and SPP1 were markedly enriched in the CAFs and macrophages of CRC tissues, respectively. Moreover, we identified the significantly enriched gene sets in LM-CRC, especially those in the SPP1+macrophages and ANGPTL2+CAFs, such as the HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION and the HALLMARK_COMPLEMENT. Finally, our in vitro experiments proved that ANGPTL2+CAFs and SPP1+macrophages promote the metastasis of CRC cells.

Conclusion

Our study selected four characteristic genes of LM-CRC and identified ANGPTL2+CAFs and SPP1+macrophages subtypes as metastasis accelerators of CRC which provided a potential therapeutic target for LM-CRC.

Opportunities and challenges in the application of single-cell and spatial transcriptomics in plants

Single-cell and spatial transcriptomics have diverted researchers' attention from the multicellular level to the single-cell level and spatial information. Single-cell transcriptomes provide insights into the transcriptome at the single-cell level, whereas spatial transcriptomes help preserve spatial information. Although these two omics technologies are helpful and mature, further research is needed to ensure their widespread applicability in plant studies. Reviewing recent research on plant single-cell or spatial transcriptomics, we compared the different experimental methods used in various plants. The limitations and challenges are clear for both single-cell and spatial transcriptomic analyses, such as the lack of applicability, spatial information, or high resolution. Subsequently, we put forth further applications, such as cross-species analysis of roots at the single-cell level and the idea that single-cell transcriptome analysis needs to be combined with other omics analyses to achieve superiority over individual omics analyses. Overall, the results of this review suggest that combining single-cell transcriptomics, spatial transcriptomics, and spatial element distribution can provide a promising research direction, particularly for plant research.

The expression and prognostic value of disulfidptosis progress in lung adenocarcinoma

Disulfidptosis is a new cell death model caused by accumulating intracellular disulfides bonding to actin cytoskeleton proteins. This study aimed to investigate the expression and prognostic value of disulfidptosis-related genes (DRGs) in lung adenocarcinoma (LUAD). The data of expression profiles and scRNA-seq were collected from TCGA and GEO databases. The different expressions of DRGs between normal and LUAD tissues were compared. The LASSO analysis and multivariate Cox regression analysis were utilized to develop a DRGs model for the prognosis evaluation in LUAD. The model's predictive accuracy was evaluated with the area under the receiver operating characteristic curve (AUC) and C-index. Survival analysis, univariate and multivariate Cox regression analysis were used to assessing the predictive value of the DRGs model. ScRNA-seq data were analyzed with "Seurat" and "Monocle 2" packages. There were significant differences in 22 DRGs between normal and tumor tissues. A model with five DRGs (ACTB, FLNB, NCKAP1, SLC3A2, SLC7A11) was constructed. The AUC and C-index of the model were significantly higher than that based on clinical parameters. Survival analysis, univariate and multivariate Cox regression analysis demonstrated risk score was an independent prognostic predictor. In the scRNA-seq study, we identified 14 clusters and 11 cell types. Clusters 2, 8, and 13 were annotated into Epithelial cells. SLC7A11 and SLC3A2, NCKAP1 and FLNB, ACTB expressed most abundantly in Epithelial cells, Endothelial cells, Naive CD4 T, respectively. We explored the expression of DRGs in LUAD and constructed a predictive DRGs model, which was stable and reliable for predicting LUAD prognosis.

Preparation of a Single-Cell Suspension from Tumor Biopsy Samples for Single-Cell RNA Sequencing

An essential requirement for single-cell RNA sequencing in cancer is the preparation of high-quality single-cell suspensions from the tumor tissue. In this work, various methods of dissociation of tumor biopsy specimens were analyzed and developed to obtain a cell suspension with at least 80% viability. It was found that the optimal conditions for sample preparation are mechanical dissociation followed by incubation with a collagenase/hyaluronidase mixture with addition of DNAase I for 60 min. Thus, we optimize the approach for preparing single-cell suspensions from the tumor biopsy tissue for single-cell RNA sequencing.

Enhanced oxidative phosphorylation, re-organized intracellular signaling, and epigenetic de-silencing as revealed by oligodendrocyte translatome analysis after contusive spinal cord injury

Reducing the loss of oligodendrocytes (OLs) is a major goal for neuroprotection after spinal cord injury (SCI). Therefore, the OL translatome was determined in Ribotag:Plp1-CreERT2 mice at 2, 10, and 42 days after moderate contusive T9 SCI. At 2 and 42 days, mitochondrial respiration- or actin cytoskeleton/cell junction/cell adhesion mRNAs were upregulated or downregulated, respectively. The latter effect suggests myelin sheath loss/morphological simplification which is consistent with downregulation of cholesterol biosynthesis transcripts on days 10 and 42. Various regulators of pro-survival-, cell death-, and/or oxidative stress response pathways showed peak expression acutely, on day 2. Many acutely upregulated OL genes are part of the repressive SUZ12/PRC2 operon suggesting that epigenetic de-silencing contributes to SCI effects on OL gene expression. Acute OL upregulation of the iron oxidoreductase Steap3 was confirmed at the protein level and replicated in cultured OLs treated with the mitochondrial uncoupler FCCP. Hence, STEAP3 upregulation may mark mitochondrial dysfunction. Taken together, in SCI-challenged OLs, acute and subchronic enhancement of mitochondrial respiration may be driven by axonal loss and subsequent myelin sheath degeneration. Acutely, the OL switch to oxidative phosphorylation may lead to oxidative stress that is further amplified by upregulation of such enzymes as STEAP3.

Comparative analyses of the prognosis, tumor immune microenvironment, and drug treatment response between left-sided and right-sided colon cancer by integrating scRNA-seq and bulk RNA-seq data

BACKGROUND

In this study, we compared the prognosis, tumor immune microenvironment (TIM), and drug treatment response between left-sided (LCC) and right-sided (RCC) colon cancer to predict outcomes in patients with LCC and RCC.

METHODS

Based on identified differentially expressed genes and using single-cell RNA sequencing data, we constructed and validated a prognostic model for LCC and RCC patients in the TCGA-COAD cohort and GSE103479 cohort. Moreover, we compared the differences of TIM characteristics and drug treatment response between LCC and RCC patients.

RESULTS

We constructed and validated a five-gene prognostic model for LCC patients and a four-gene prognostic model for RCC patients, and both showed excellent performance. The RCC patients with higher risk scores were significantly associated with greater metastasis (P = 2.6×10-5), N stage (P = 0.012), advanced pathological stage (P = 1.4×10-4), and more stable microsatellite status (P = 0.007) but not T stage (P = 0.200). For LCC patients, the risk scores were not significantly associated with tumor stage and microsatellite status (P > 0.05). Additionally, immune infiltration by CD8 and regulatory T cells and M0, M1, and M2 macrophages differed significantly between LCC and RCC patients (P < 0.05). APC and TP53 mutations were significantly more common in LCC patients (P < 0.05). In contrast, KRAS, SYNE1, and MUC16 mutations were significantly more common in RCC patients (P < 0.05). In addition, tumor mutation burden values were significantly higher in RCC patients than in LCC patients (P = 5.9×10-8). Moreover, the expression of immune checkpoint targets was significantly higher in RCC patients than in LCC patients (P < 0.05), indicating that RCC patients maybe more sensitive to immunotherapy. However, LCC and RCC patients did not differ significantly in their sensitivity to eight selected chemicals or target drugs (P > 0.05). The average half-maximal inhibitory concentrations for camptothecin, teniposide, vinorelbine, and mitoxantrone were significantly lower in low-risk than in high-risk RCC patients (P < 0.05), indicating that the lower risk score of RCC patients, the more sensitive they were to these four drugs.

CONCLUSIONS

We investigated the differences in prognosis, TIM, and drug treatment response between LCC and RCC patients, which may contribute to accurate colon cancer prognosis and treatment of colon cancer.

Identification of cell subpopulations associated with disease phenotypes from scRNA-seq data using PACSI

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) has revolutionized the transcriptomics field by advancing analyses from tissue-level to cell-level resolution. Despite the great advances in the development of computational methods for various steps of scRNA-seq analyses, one major bottleneck of the existing technologies remains in identifying the molecular relationship between disease phenotype and cell subpopulations, where "disease phenotype" refers to the clinical characteristics of each patient sample, and subpopulation refer to groups of single cells, which often do not correspond to clusters identified by standard single-cell clustering analysis. Here, we present PACSI, a method aimed at distinguishing cell subpopulations associated with disease phenotypes at the single-cell level.

RESULTS

PACSI takes advantage of the topological properties of biological networks to introduce a proximity-based measure that quantifies the correlation between each cell and the disease phenotype of interest. Applied to simulated data and four case studies, PACSI accurately identified cells associated with disease phenotypes such as diagnosis, prognosis, and response to immunotherapy. In addition, we demonstrated that PACSI can also be applied to spatial transcriptomics data and successfully label spots that are associated with poor survival of breast carcinoma.

CONCLUSIONS

PACSI is an efficient method to identify cell subpopulations associated with disease phenotypes. Our research shows that it has a broad range of applications in revealing mechanistic and clinical insights of diseases.

The Contribution of Multiplexing Single Cell RNA Sequencing in Acute Myeloid Leukemia

Decades ago, the treatment for acute myeloid leukemia relied on cytarabine and anthracycline. However, advancements in medical research have introduced targeted therapies, initially employing monoclonal antibodies such as ant-CD52 and anti-CD123, and subsequently utilizing specific inhibitors that target molecular mutations like anti-IDH1, IDH2, or FLT3. The challenge lies in determining the role of these therapeutic options, considering the inherent tumor heterogeneity associated with leukemia diagnosis and the clonal drift that this type of tumor can undergo. Targeted drugs necessitate an examination of various therapeutic targets at the individual cell level rather than assessing the entire population. It is crucial to differentiate between the prognostic value and therapeutic potential of a specific molecular target, depending on whether it is found in a terminally differentiated cell with limited proliferative potential or a stem cell with robust capabilities for both proliferation and self-renewal. However, this cell-by-cell analysis is accompanied by several challenges. Firstly, the scientific aspect poses difficulties in comparing different single cell analysis experiments despite efforts to standardize the results through various techniques. Secondly, there are practical obstacles as each individual cell experiment incurs significant financial costs and consumes a substantial amount of time. A viable solution lies in the ability to process multiple samples simultaneously, which is a distinctive feature of the cell hashing technique. In this study, we demonstrate the applicability of the cell hashing technique for analyzing acute myeloid leukemia cells. By comparing it to standard single cell analysis, we establish a strong correlation in various parameters such as quality control, gene expression, and the analysis of leukemic blast markers in patients. Consequently, this technique holds the potential to become an integral part of the biological assessment of acute myeloid leukemia, contributing to the personalized and optimized management of the disease, particularly in the context of employing targeted therapies.

Single-cell RNA sequencing for the study of kidney disease

The kidney is an important organ for maintaining normal metabolism and stabilising the internal environment, in which, the heterogeneity of cell types has hindered the progress in understanding the mechanisms underlying kidney disease. In recent years the application of single-cell RNA sequencing (scRNA-seq) in nephrology has developed rapidly. In this review, we summarized the technical platform related to scRNA-seq and the role of this technology in investigating the onset and development of kidney diseases, starting from several common kidney diseases (mainly including lupus nephritis, renal cell carcinoma, diabetic nephropathy and acute kidney injury), and provide a reference for the application of scRNA-seq in the study of kidney disease diagnosis, treatment and prognosis.

Robust reconstruction of single-cell RNA-seq data with iterative gene weight updates

MOTIVATION

Single-cell RNA-sequencing technologies have greatly enhanced our understanding of heterogeneous cell populations and underlying regulatory processes. However, structural (spatial or temporal) relations between cells are lost during cell dissociation. These relations are crucial for identifying associated biological processes. Many existing tissue-reconstruction algorithms use prior information about subsets of genes that are informative with respect to the structure or process to be reconstructed. When such information is not available, and in the general case when the input genes code for multiple processes, including being susceptible to noise, biological reconstruction is often computationally challenging.

RESULTS

We propose an algorithm that iteratively identifies manifold-informative genes using existing reconstruction algorithms for single-cell RNA-seq data as subroutine. We show that our algorithm improves the quality of tissue reconstruction for diverse synthetic and real scRNA-seq data, including data from the mammalian intestinal epithelium and liver lobules.

AVAILABILITY AND IMPLEMENTATION

The code and data for benchmarking are available at github.com/syq2012/iterative_weight_update_for_reconstruction.

Role of the kidneys in acid-base regulation and ammonia excretion in freshwater and seawater fish: implications for nephrocalcinosis

Maintaining normal pH levels in the body fluids is essential for homeostasis and represents one of the most tightly regulated physiological processes among vertebrates. Fish are generally ammoniotelic and inhabit diverse aquatic environments that present many respiratory, acidifying, alkalinizing, ionic and osmotic stressors to which they are able to adapt. They have evolved flexible strategies for the regulation of acid-base equivalents (H⁺, NH₄ ⁺, OH^- and HCO₃ ^-), ammonia and phosphate to cope with these stressors. The gills are the main regulatory organ, while the kidneys play an important, often overlooked accessory role in acid-base regulation. Here we outline the kidneys role in regulation of acid-base equivalents and two of the key 'urinary buffers', ammonia and phosphate, by integrating known aspects of renal physiology with recent advances in the molecular and cellular physiology of membrane transport systems in the teleost kidneys. The renal transporters (NHE3, NBC1, AE1, SLC26A6) and enzymes (V-type H⁺ATPase, CAc, CA IV, ammoniagenic enzymes) involved in H⁺ secretion, bicarbonate reabsorption, and the net excretion of acidic and basic equivalents, ammonia, and inorganic phosphate are addressed. The role of sodium-phosphate cotransporter (Slc34a2b) and rhesus (Rh) glycoproteins (ammonia channels) in conjunction with apical V-type H⁺ ATPase and NHE3 exchangers in these processes are also explored. Nephrocalcinosis is an inflammation-like disorder due to the precipitation of calcareous material in the kidneys, and is listed as one of the most prevalent pathologies in land-based production of salmonids in recirculating aquaculture systems. The causative links underlying the pathogenesis and etiology of nephrocalcinosis in teleosts is speculative at best, but acid-base perturbation is probably a central pathophysiological cause. Relevant risk factors associated with nephrocalcinosis are hypercapnia and hyperoxia in the culture water. These raise internal CO₂ levels in the fish, triggering complex branchial and renal acid-base compensations which may promote formation of kidney stones. However, increased salt loads through the rearing water and the feed may increase the prevalence of nephrocalcinosis. An increased understanding of the kidneys role in acid-base and ion regulation and how this relates to renal diseases such as nephrocalcinosis will have applied relevance for the biologist and aquaculturist alike.

DCATS: differential composition analysis for flexible single-cell experimental designs

Differential composition analysis - the identification of cell types that have statistically significant changes in abundance between multiple experimental conditions - is one of the most common tasks in single cell omic data analysis. However, it remains challenging to perform differential composition analysis in the presence of flexible experimental designs and uncertainty in cell type assignment. Here, we introduce a statistical model and an open source R package, DCATS, for differential composition analysis based on a beta-binomial regression framework that addresses these challenges. Our empirical evaluation shows that DCATS consistently maintains high sensitivity and specificity compared to state-of-the-art methods.

Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing Data

Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease in children. The heterogeneity of the disease can be investigated via single-cell RNA sequencing (scRNA-seq) for its gap in the literature. Firstly, five types of immune cells (plasma cells, naive CD4 T cells, memory-activated CD4 T cells, eosinophils, and neutrophils) were significantly different between normal control (NC) and JIA samples. WGCNA was performed to identify genes that exhibited the highest correlation to differential immune cells. Then, 168 differentially expressed immune cell-related genes (DE-ICRGs) were identified by overlapping 13,706 genes identified by WGCNA and 286 differentially expressed genes (DEGs) between JIA and NC specimens. Next, four key genes, namely SOCS3, JUN, CLEC4C, and NFKBIA, were identified by a protein-protein interaction (PPI) network and three machine learning algorithms. The results of functional enrichment revealed that SOCS3, JUN, and NFKBIA were all associated with hallmark TNF-α signaling via NF-κB. In addition, cells in JIA samples were clustered into four groups (B cell, monocyte, NK cell, and T cell groups) by single-cell data analysis. CLEC4C and JUN exhibited the highest level of expression in B cells; NFKBIA and SOCS3 exhibited the highest level of expression in monocytes. Finally, real-time quantitative PCR (RT-qPCR) revealed that the expression of three key genes was consistent with that determined by differential analysis. Our study revealed four key genes with prognostic value for JIA. Our findings could have potential implications for JIA treatment and investigation.

Single-cell sequencing in primary intraocular tumors: understanding heterogeneity, the microenvironment, and drug resistance

Retinoblastoma (RB) and uveal melanoma (UM) are the most common primary intraocular tumors in children and adults, respectively. Despite continued increases in the likelihood of salvaging the eyeball due to advancements in local tumor control, prognosis remains poor once metastasis has occurred. Traditional sequencing technology obtains averaged information from pooled clusters of diverse cells. In contrast, single-cell sequencing (SCS) allows for investigations of tumor biology at the resolution of the individual cell, providing insights into tumor heterogeneity, microenvironmental properties, and cellular genomic mutations. SCS is a powerful tool that can help identify new biomarkers for diagnosis and targeted therapy, which may in turn greatly improve tumor management. In this review, we focus on the application of SCS for evaluating heterogeneity, microenvironmental characteristics, and drug resistance in patients with RB and UM.

Revealing the Organ-Specific Expression of SPTBN1 using Single-Cell RNA Sequencing Analysis

Despite the recent technological advances in single-cell RNA sequencing, it is still unknown how three marker genes (SPTBN1, EPDR1, and PKDCC), which are associated with bone fractures and highly expressed in the muscle tissue, are contributing to the development of other tissues and organs at the cellular level. This study aims to analyze three marker genes at the single-cell level using 15 organ tissue types of adult human cell atlas (AHCA). The single-cell RNA sequencing analysis used three marker genes and a publicly available AHCA data set. AHCA data set contains more than 84,000 cells from 15 organ tissue types. Quality control filtering, dimensionality reduction, clustering for cells, and data visualization were performed using the Seurat package. A total of 15 organ types are included in the downloaded data sets: Bladder, Blood, Common Bile Duct, Esophagus, Heart, Liver, Lymph Node, Marrow, Muscle, Rectum, Skin, Small Intestine, Spleen, Stomach, and Trachea. In total, 84,363 cells and 228,508 genes were included in the integrated analysis. A marker gene of SPTBN1 is highly expressed across all 15 organ types, particularly in the Fibroblasts, Smooth muscle cells, and Tissue stem cells of the Bladder, Esophagus, Heart, Muscle, Rectum, Skin, and Trachea. In contrast, EPDR1 is highly expressed in the Muscle, Heart, and Trachea, and PKDCC is only expressed in Heart. In conclusion, SPTBN1 is an essential protein gene in physiological development and plays a critical role in the high expression of fibroblasts in multiple organ types. Targeting SPTBN1 may prove beneficial for fracture healing and drug discovery.

Integration single-cell and bulk RNA-sequencing data to reveal senescence gene expression profiles in heart failure

Background

Heart failure (HF) represents one of healthcare's biggest challenges. Although rarely noticed, aging is a crucial risk factor for cardiovascular disease. Our study aims to reveal aging's role in HF by integrating single-cell RNA-sequencing (scRNA-seq) and bulk RNA-sequencing databases.

Methods

We collected HF heart sample data from the Gene Expression Omnibus database and senescence gene data from CellAge. The FindCluster () package was used for cell cluster analysis. Differentially expressed genes (DEG) were identified operating the FindMarkers function. Cell activity score calculation was performed using the AUCell package. UpSetR plotted the intersection between DEGs of active cell types, bulk data DEGs, and genes associated with aging. Using the DGIdb database gene-drug interaction data, we search for potential targeted therapeutics based on common senescence genes.

Results

The scRNA-seq data revealed myocardial heterogeneity in HF tissues. A series of crucial common senescence genes were found. The senescence gene expression profile hints at an intriguing connection between monocytes and HF. After analyzing the DEGs in the bulk dataset, the DEGs in scRNA-seq, the DEGs in each active cell type, and senescence genes, we identified ten genes as common senescence genes present in HF. Correlation analysis of transcriptomics, proteomics, and ceRNA was performed to provide ideas for future studies individually. Moreover, we discovered that common senescence genes and potential therapeutic drugs interact among different cell types. Further research is needed on the expression pattern of senescence genes and molecular regulation in HF.

Conclusions

In summary, we identified the functional significance of the senescence gene in HF using integrated data. It is possible that this more profound understanding of how senescence contributes to the development of HF will aid in unraveling the mechanisms that promote the disease and provide hints for developing therapeutics.

Single-cell RNA sequencing in skeletal muscle developmental biology

Skeletal muscle is the most extensive tissue in mammals, and they perform several functions; it is derived from paraxial mesodermal somites and undergoes hyperplasia and hypertrophy to form multinucleated, contractile, and functional muscle fibers. Skeletal muscle is a complex heterogeneous tissue composed of various cell types that establish communication strategies to exchange biological information; therefore, characterizing the cellular heterogeneity and transcriptional signatures of skeletal muscle is central to understanding its ontogeny's details. Studies of skeletal myogenesis have focused primarily on myogenic cells' proliferation, differentiation, migration, and fusion and ignored the intricate network of cells with specific biological functions. The rapid development of single-cell sequencing technology has recently enabled the exploration of skeletal muscle cell types and molecular events during development. This review summarizes the progress in single-cell RNA sequencing and its applications in skeletal myogenesis, which will provide insights into skeletal muscle pathophysiology.

Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.

Highly efficient cell-microbead encapsulation using dielectrophoresis-assisted dual-nanowell array

Recent advancements in micro/nanofabrication techniques have led to the development of portable devices for high-throughput single-cell analysis through the isolation of individual target cells, which are then paired with functionalized microbeads. Compared with commercially available benchtop instruments, portable microfluidic devices can be more widely and cost-effectively adopted in single-cell transcriptome and proteome analysis. The sample utilization and cell pairing rate (∼33%) of current stochastic-based cell-bead pairing approaches are fundamentally limited by Poisson statistics. Despite versatile technologies having been proposed to reduce randomness during the cell-bead pairing process in order to statistically beat the Poisson limit, improvement of the overall pairing rate of a single cell to a single bead is typically based on increased operational complexity and extra instability. In this article, we present a dielectrophoresis (DEP)-assisted dual-nanowell array (ddNA) device, which employs an innovative microstructure design and operating process that decouples the bead- and cell-loading processes. Our ddNA design contains thousands of subnanoliter microwell pairs specifically tailored to fit both beads and cells. Interdigitated electrodes (IDEs) are placed below the microwell structure to introduce a DEP force on cells, yielding high single-cell capture and pairing rates. Experimental results with human embryonic kidney cells confirmed the suitability and reproducibility of our design. We achieved a single-bead capture rate of >97% and a cell-bead pairing rate of >75%. We anticipate that our device will enhance the application of single-cell analysis in practical clinical use and academic research.