Single-cell Expression Profiling Research Articles

OneSC: A computational platform for recapitulating cell state transitions.

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate cell state transitions using systems of stochastic differential equations govern by a regulatory network of core transcription factors (TFs). Different from many current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and terminal cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations. OneSC is implemented as a Python package on GitHub (https://github.com/CahanLab/oneSC) and on Zenodo (https://zenodo.org/records/14052421). Supplementary data are available at Bioinformatics online.

The mechanism underlying B-cell developmental dysfunction in Kawasaki disease based on single-cell transcriptomic sequencing.

Kawasaki disease (KD) is an acute systemic vasculitis that can lead to acquired heart disease in children mostly from in developed countries. The previous research showed that B cells in KD patients underwent a profound change in both the cell numbers and types after intravenous immunoglobulin (IVIG) therapy. We performed the single-cell RNA-sequencing for the peripheral blood mononuclear cells (PBMCs) from three febrile patients and three KD patients to investigate the possible mechanism underlying B cell developmental dysfunction in KD. The pseudo-time analysis was employed to study the developmental trajectories of the PBMCs in febrile control and KD patients. Overall single-cell expression profiles show that the biological processes of immunity, B cell activation pathway and their related biological entities are repressed in KD patients before IVIG treatment compared to febrile patient and KD patients after IVIG treatment. The differentially expressed gene analyses further demonstrate that B cell signaling pathway is downregulated in B cells and plasma blast cells of KD patients before treatment while cell cycle genes and MYC gene are upregulated in dendritic cells (DCs) and hematopoietic stem and progenitor cells (HSPCs) of KD patients before treatment. The biological process of immune response is upregulated in the HSPCs of KD patients before treatment in our dataset while the biological process of inflammatory response is upregulated in the HSPCs of KD patients before treatment in GSE168732 dataset. Single-cell trajectory analyses demonstrate that KD patients before treatment have a shortened developmental path in which B cells and T cells are failed to differentiate into separate lineages. HSPD1 and HSPE1 genes show an elevated expression level in the early cell development stage of KD patients before treatment accompanied with the repression of MYC, SPI1, MT2A and UBE2C genes. Our analyses of all B cells from KD patients before treatment show most of B cells are arrested in a transitional state with an ill developmental path compared with febrile patients and KD patients after treatment. Our results indicate that the immune premature HSPCs accompanied with the abnormal expression dynamics of cell cycle and SPI1 genes are the mechanism underlying B cell developmental dysfunction in KD patients.

Single-cell expression profiling has its roots in in situ techniques.

Single-cell expression profiling has its roots in in situ techniques.

Recovering single-cell expression profiles from spatial transcriptomics with scResolve.

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained throughscResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

Targeted single cell expression profiling identifies integrators of sleep and metabolic state.

Animals modulate sleep in accordance with their internal and external environments. Metabolic cues are particularly potent regulators of sleep, allowing animals to alter their sleep timing and amount depending on food availability and foraging duration. The fruit fly, Drosophila melanogaster, suppresses sleep in response to acute food deprivation, presumably to forage for food. This process is dependent on a single pair of Lateral Horn Leucokinin (LHLK) neurons, that secrete the neuropeptide Leucokinin. These neurons signal to insulin producing cells and suppress sleep under periods of starvation. The identification of individual neurons that modulate sleep-metabolism interactions provides the opportunity to examine the cellular changes associated with sleep modulation. Here, we use single-cell sequencing of LHLK neurons to examine the transcriptional responses to starvation. We validate that a Patch-seq approach selectively isolates RNA from individual LHLK neurons. Single-cell CEL-Seq comparisons of LHLK neurons between fed and 24-hr starved flies identified 24 genes that are differentially expressed in accordance with starvation state. In total, 12 upregulated genes and 12 downregulated genes were identified. Gene-ontology analysis showed an enrichment for Attacins, a family of anti-microbial peptides, along with several transcripts with diverse roles in regulating cellular function. Targeted knockdown of differentially expressed genes identified multiple genes that function within LHLK neurons to regulate sleep-metabolism interactions. Functionally validated genes include an essential role for the E3 ubiquitin Ligase insomniac, the sorbitol dehydrogenase Sodh1, as well as AttacinC and AttacinB in starvation-induced sleep suppression. Taken together, these findings provide a pipeline for identifying novel regulators of sleep-metabolism interactions within individual neurons.

ScDTL: enhancing single-cell RNA-seq imputation through deep transfer learning with bulk cell information.

The increasing single-cell RNA sequencing (scRNA-seq) data enable researchers to explore cellular heterogeneity and gene expression profiles, offering a high-resolution view of the transcriptome at the single-cell level. However, the dropout events, which are often present in scRNA-seq data, remaining challenges for downstream analysis. Although a number of studies have been developed to recover single-cell expression profiles, their performance may be hindered due to not fully exploring the inherent relations between genes. To address the issue, we propose scDTL, a deep transfer learning based approach for scRNA-seq data imputation by harnessing the bulk RNA-sequencing information. We firstly employ a denoising autoencoder trained on bulk RNA-seq data as the initial imputation model, and then leverage a domain adaptation framework that transfers the knowledge learned by the bulk imputation model to scRNA-seq learning task. In addition, scDTL employs a parallel operation with a 1D U-Net denoising model to provide gene representations of varying granularity, capturing both coarse and fine features of the scRNA-seq data. Finally, we utilize a cross-channel attention mechanism to fuse the features learned from the transferred bulk imputation model and U-Net model. In the evaluation, we conduct extensive experiments to demonstrate that scDTL could outperform other state-of-the-art methods in the quantitative comparison and downstream analyses.

Identification of the tumor metastasis-related tumor subgroups overexpressed NENF in triple-negative breast cancer by single-cell transcriptomics

Tumor metastasis is a continuous and dynamic process and is a major cause of tumor-related death in triple-negative breast cancer. However, this biological process remains largely unknown in triple-negative breast cancer. The emergence of single-cell sequencing enables a deeper understanding of the tumor microenvironment and provides a new strategy for discovering the potential mechanism of tumor metastasis. Herein, we integrated the single-cell expression profiling of primary and metastatic triple-negative breast cancer by Seurat package. Nine tumor cell subgroups were identified. Enrichment analysis suggested tumor subgroups (C0, C4) were associated with tumor metastasis with poor prognosis in TNBC. Weighted gene co-expression network was constructed and identified NENF was a metastasis-related gene. Subsequently, RT-qPCR, Immunohistochemistry, and western blot confirmed NENF is highly expressed in TNBC tissues. And cell function assays indicated NENF promote cell invasion and migration through regulating EMT in TNBC. Finally, TIDE and Connectivity Map database suggest the candidate drugs for targeting NENF. In conclusion, our findings provide a new insight into the progression and metastasis of TNBC and uncover NENF may be a prognostic biomarker and potential therapy targets.

Multiscale mapping of transcriptomic signatures for cardiotoxic drugs

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification.

439. INTEGRATION OF SCRNA-SEQ AND BULK RNA-SEQ TO VALIDATE A PROGNOSTIC ASSOCIATED SIGNATURE IN ESOPHAGEAL SQUAMOUS CELL CARCINOMA

Abstract Background Considerable evidence has identified important targets for esophageal squamous cell carcinoma (ESCC), but its efficacy and the heterogeneity of ESCC are the main causes of treatment failure. Single-cell RNA-sequencing (scRNA-seq) is a powerful tool for analyzing tumor heterogeneity at the single-cell level and helps to better identify potential therapeutic targets. Methods ESCC scRNA-seq data were extracted from the Gene Expression Omnibus (GEO) database. Single-sample gene set enrichment analysis (ssGSEA) was performed using the limma package to analyze ESCC-associated prognostic marker genes from ESCC transcriptome data downloaded from the TCGA database and the results were validated with transcriptomic data from 56 ESCC patients in our hospital. KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses of prognostic marker genes were performed by GSEA. Then, univariate and multivariate Cox and lasso regressions were performed to further build signatures. Finally, qPCR was used to assess the expression of prognostic marker genes in 5 pairs ESCC samples. Results Integrate scRNA-seq (GSE53624) datasets to obtain 13 cell clusters. We characterized single-cell expression profiles of 24076 cell samples and identified 4 marker genes associated with prognosis. Four key prognostic marker genes were trained and validated on the TCGA-ESCA dataset and in 55 patients from our in-hospital cohort to evaluate predictive overall survival (OS) models. Correlation analysis showed that prognostic marker genes were independent of risk clinical characteristics. In addition, we performed Cox regression analysis, suggesting that the 4 prognostic marker genes screened were independent risk factors related to the prognosis of ESCC patients. In addition, qPCR showed that the expression of IER3 and SAA1 was decreased and the expression of CDKN3 was increased in ESCC tissues, while the expression of TINP3 was not significantly different. Conclusion We constructed prognostic marker genes by scRNA-seq and validated the model in a large patient population as well as in ESCC tissues, which provides reasonable evidence and valuable resources for prognostic stratification and the study of potential targets for ESCC.

Cross-species imputation and comparison of single-cell transcriptomic profiles.

Cross-species comparison and prediction of gene expression profiles are important to understand regulatory changes during evolution and to transfer knowledge learned from model organisms to humans. Single-cell RNA-seq (scRNA-seq) profiles enable us to capture gene expression profiles with respect to variations among individual cells; however, cross-species comparison of scRNA-seq profiles is challenging because of data sparsity, batch effects, and the lack of one-to-one cell matching across species. Moreover, single-cell profiles are challenging to obtain in certain biological contexts, limiting the scope of hypothesis generation. Here we developed Icebear, a neural network framework that decomposes single-cell measurements into factors representing cell identity, species, and batch factors. Icebear enables accurate prediction of single-cell gene expression profiles across species, thereby providing high-resolution cell type and disease profiles in under-characterized contexts. Icebear also facilitates direct cross-species comparison of single-cell expression profiles for conserved genes that are located on the X chromosome in eutherian mammals but on autosomes in chicken. This comparison, for the first time, revealed evolutionary and diverse adaptations of X-chromosome upregulation in mammals.

Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention auto-encoder.

Single-cell multi-omics integration enables joint analysis at the single-cell level of resolution to provide more accurate understanding of complex biological systems, while spatial multi-omics integration is benefit to the exploration of cell spatial heterogeneity to facilitate more comprehensive downstream analyses. Existing methods are mainly designed for single-cell multi-omics data with little consideration of spatial information and still have room for performance improvement. A reliable multi-omics integration method designed for both single-cell and spatially resolved data is necessary and significant. We propose a multi-omics integration method based on dual-path graph attention auto-encoder (SSGATE). It can construct the neighborhood graphs based on single-cell expression profiles or spatial coordinates, enabling it to process single-cell data and utilize spatial information from spatially resolved data. It can also perform self-supervised learning for integration through the graph attention auto-encoders from two paths. SSGATE is applied to integration of transcriptomics and proteomics, including single-cell and spatially resolved data of various tissues from different sequencing technologies. SSGATE shows better performance and stronger robustness than competitive methods and facilitates downstream analysis.

Non-invasive determination of gene expression in placental tissue using maternal plasma cell-free DNA fragmentation characters

BackgroundThe expression profiles of placental genes are crucial for understanding the pathogenesis of fetal development and placental-origin pregnancy syndromes. However, owing to ethical limitations and the risks of puncture sampling, it is difficult to obtain placental tissue samples repeatedly, continuously, multiple times, or in real time. Establishing a non-invasive method for predicting placental gene expression profiles through maternal plasma cell-free DNA (cfDNA) sequencing, which carries information about the source tissue and gene expression, can potentially solve this problem. MethodsPeripheral blood and placental samples were collected simultaneously from pregnant women who underwent cesarean section. Deep sequencing was performed on the separated plasma cfDNA and single-cell sequencing was performed on peripheral blood mononuclear cells (PBMC), chorioamniotic membranes (CAM), placental villi (PV), and decidua basalis (DB). The aggregation of corresponding information for each gene was combined with the transcriptome of PBMCs and a differential resolution transcriptome of the placenta. This combined information was then utilized for the construction of gene expression prediction models. After training, all models evaluated the correlation between the predicted and actual gene expression levels using external test set data. ResultsFrom five women, more than 20 million reads were obtained using deep sequencing for plasma cfDNA; PBMCs obtained 32,401 single-cell expression profiles; and placental tissue obtained 156,546 single-cell expression profiles (59,069, 44,921, and 52,556 for CAM, PV, and DB, respectively). The cells in the PBMC and placenta were clustered and annotated into five and eight cell types, respectively. A “DEPICT” gene expression prediction model was successfully constructed using deep neural networks. The predicted correlation coefficients were 0.75 in PBMCs, 0.84 in the placenta, and 0.78, 0.80, and 0.77 in CAM, BP, and PV respectively, and greater than 0.68 in different cell lines in the placenta. ConclusionThe DEPICT model, which can noninvasively predict placental gene expression profiles based on maternal plasma cfDNA fragmentation characteristics, was constructed to overcome the limitation of the inability to obtain real-time placental gene expression profiles and to improve research on noninvasive prediction of placental origin pregnancy syndrome.

Comparative single-cell transcriptomic analysis reveals putative differentiation drivers and potential origin of vertebrate retina.

Despite known single-cell expression profiles in vertebrate retinas, understanding of their developmental and evolutionary expression patterns among homologous cell classes remains limited. We examined and compared approximately 240 000 retinal cells from four species and found significant similarities among homologous cell classes, indicating inherent regulatory patterns. To understand these shared patterns, we constructed gene regulatory networks for each developmental stage for three of these species. We identified 690 regulons governed by 530 regulators across three species, along with 10 common cell class-specific regulators and 16 highly preserved regulons. RNA velocity analysis pinpointed conserved putative driver genes and regulators to retinal cell differentiation in both mouse and zebrafish. Investigation of the origins of retinal cells by examining conserved expression patterns between vertebrate retinal cells and invertebrate Ciona intestinalis photoreceptor-related cells implied functional similarities in light transduction mechanisms. Our findings offer insights into the evolutionarily conserved regulatory frameworks and differentiation drivers of vertebrate retinal cells.

DIPG-51. DIFFUSE MIDLINE GLIOMAS HIJACK MYELOID CELL POPULATIONS TO INITIATE AN ANTI-INFLAMMATORY TUMOR IMMUNE MICROENVIRONMENT

Abstract BACKGROUND Diffuse midline gliomas (DMG) are universally fatal pediatric brain tumors, for which no effective treatment exists. These tumors are known for their “cold” non-inflammatory tumor-immune microenvironment (TIME), which is thought to be a major hurdle for immunotherapy efficacy. METHODS/RESULTS To further investigate the mechanisms that drive this cold environment, we allografted immunocompetent mice with Histone 3.3 Lysine 27 (H3.3K27M) and 3.1 Lysine 27 (H3.1K27M) mutant murine DMG tumors, created through intra uterine electroporation, and performed single-cell analyses on the tumors that developed. Using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITEseq) we mapped the single-cell RNA and protein expression profiles of the TIME of these tumors and healthy pontine regions, with a dominant focus on the myeloid cell populations. Through differential expression and pathway enrichment analyses, we found that DMG-microglia populations upregulate anti-inflammatory pathways and, over time, are pushed out by infiltrating anti-inflammatory macrophages. We confirmed these findings in single-cell sequencing data from DMG patients, taken at diagnosis and at late stage disease. Next, we established microglia cultures in vitro, by differentiating monocytes from healthy donors into microglia. We discovered that treatment of these microglia with tumor-conditioned medium, taken from primary DMG cultures, diminished their pro-inflammatory potential after stimulation with lipopolysaccharides (LPS), as observed by cytokine release arrays that showed a strong downregulation of IL-6 and TNF-alpha release, amongst others. Subsequently, using a Seahorse metabolic analyzer, we found that glycoATP potential was diminished in DMG-primed microglia after LPS stimulation, further suggesting that DMG-excreted factors are responsible for blocking the pro-inflammatory potential of microglia. CONCLUSIONS Taken together, these findings increase our understanding on how DMG cells reprogram tumor-associated myeloid cells to generate an immune-cold TIME, which will aid the scientific community to find therapeutic solutions to reverse this phenomenon and improve the efficacy of immunotherapy in these devastating malignancies.

Rapamycin promotes the intestinal barrier repair in ulcerative colitis via the mTOR/PBLD/AMOT signaling pathway

Intestinal barrier dysfunction characterized by the functional loss of the intestinal epithelium's tight junction (TJ) barrier is a key factor in the pathogenesis of ulcerative colitis (UC). Although rapamycin, an mTOR (mechanistic target of rapamycin) inhibitor, has shown promise in inducing clinical remission and mucosal healing in inflammatory bowel disease, its underlying mechanism remains elusive. Thus, this study investigated the role of the mTOR pathway in regulating the intestinal barrier. To investigate the molecular mechanism regulating the intestinal barrier, specific intestinal epithelial phenazine biosynthesis-like domain-containing protein (PBLD)-deficient (PBLDIEC−/−) mice and control wild-type (WT) mice were intraperitoneally injected with rapamycin or MHY1485. To determine the relevance of the findings for UC, we analyzed transcriptome data and single-cell expression profiles from public databases and intestinal mucosal tissues obtained from patients with active UC or colon cancer. We observed that mTOR activation in the intestinal epithelium of patients with active UC. Moreover, in vivo, rapamycin markedly increased the expressions of PBLD and TJ proteins and reduced intestinal inflammation in mice with dextran sulfate sodium-induced enteritis. However, the therapeutic efficacy of rapamycin was notably reduced in PBLDIEC−/− mice. In vitro, rapamycin influenced PBLD expression by modulating the nuclear transcription of transcription factor EB (TFEB). Angiomotin (AMOT) could directly bind to PBLD, and rapamycin could not effectively increase the expression of TJ proteins after the knockdown of PBLD or AMOT. In summary, the administration of rapamycin is a potential treatment for UC, and targeting the mTOR/PBLD/AMOT axis is a potential novel approach for UC treatment.

OneSC: A computational platform for recapitulating cell state transitions.

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.

Investigating gene functions and single-cell expression profiles of de novo variants in orofacial clefts

Orofacial clefts (OFCs) are common congenital birth defects with various etiologies, including genetic variants. Online Mendelian Inheritance in Man (OMIM) annotated several hundred genes involving OFCs. Furthermore, several hundreds of de novo variants (DNVs) have been identified from individuals with OFCs. Some DNVs are related to known OFC genes or pathways, but there are still many DNVs whose relevance to OFC development is unknown. To explore novel gene functions and their cellular expression profiles, we focused on DNVs in genes that were not listed in OMIM. We collected 960 DNVs in 853 genes from published studies and curated these genes, based on the DNVs' deleteriousness, into 230 and 23 genes related to cleft lip with or without cleft palate (CL/P) and cleft palate only (CPO), respectively. For comparison, we curated 178 CL/P and 277 CPO genes from OMIM. In CL/P, the pathways enriched in DNV and OMIM genes were significantly overlapped (p= 0.002). Single-cell RNA sequencing (scRNA-seq) analysis of mouse lip development revealed that both gene sets had abundant expression in the ectoderm (DNV genes: adjusted p= 0.032, OMIM genes: adjusted p<0.0002), while only DNV genes were enriched in the endothelium (adjusted p= 0.032). Although we did not achieve significant findings using CPO gene sets, which was mainly due to the limited number of DNV genes, scRNA-seq analysis implicated various expression patterns among DNV and OMIM genes. Our results suggest that combinatory pathway and scRNA-seq data analyses are helpful for contextualizing genes in OFC development.

A mucus production programme promotes classical pancreatic ductal adenocarcinoma

ObjectiveThe optimal therapeutic response in cancer patients is highly dependent upon the differentiation state of their tumours. Pancreatic ductal adenocarcinoma (PDA) is a lethal cancer that harbours distinct phenotypic subtypes...

Single-cell RNA-seq analysis reveals the Wnt/Ca2+ signaling pathway with inflammation, apoptosis in nucleus pulposus degeneration

BackgroundIncreasing studies have shown degeneration of nucleus pulposus cells (NPCs) as an critical part of the progression of intervertebral disc degeneration (IVDD). However, there are relatively few studies on single-cell transcriptome contrasts in human degenerated NPCs. Moreover, differences in Wnt/Ca2+ signaling in human degenerated nucleus pulposus cells have not been elucidated. The aim of this study is to investigate the differential expression of Wnt/Ca2+ signaling pathway between normal and degenerated nucleus pulposus cells in humans and try to investigate its mechanism.MethodsWe performed bioinformatics analysis using our previously published findings to construct single cell expression profiles of normal and degenerated nucleus pulposus. Then, in-depth differential analysis was used to characterize the expression of Wnt/Ca2+ signaling pathway between normal and degenerated nucleus pulposus cells in humans.ResultsThe obtained cell data were clustered into five different chondrocytes clusters, which chondrocyte 4 and chondrocyte 5 mainly accounted for a high proportion in degenerated nucleus pulposus tissues, but rarely in normal nucleus pulposus tissues. Genes associated within the Wnt/Ca2+ signaling pathway, such as Wnt5B, FZD1, PLC (PLCB1), CaN (PPP3CA) and NAFATC1 are mainly present in chondrocyte 3, chondrocyte 4 and chondrocyte 5 from degenerated nucleus pulposus tissues. In addition, as a receptor that activates Wnt signaling pathway, LRP5 is mainly highly expressed in chondrocyte 5 of degenerated nucleus pulposus cells. Six genes, ANGPTL4, PTGES, IGFBP3, GDF15, TRIB3 and TNFRSF10B, which are associated with apoptosis and inflammatory responses, and are widespread in chondrocyte 4 and chondrocyte 5, may be closely related to degenerative of nucleus pulposus cells.ConclusionsSingle-cell RNA sequencing revealed differential expression of Wnt/Ca2+ signaling in human normal and degenerated nucleus pulposus cells, and this differential expression may be closely related to the abundance of chondrocyte 4 and chondrocyte 5 in degenerated nucleus pulposus cells. In degenerated nucleus pulposus cells, LRP5 activate Wnt5B, which promotes nucleus pulposus cell apoptosis and inflammatory response by regulating the Wnt/Ca2+ signaling pathway, thereby promoting disc degeneration. ANGPTL4, IGFBP3, PTGES in chondrocyte 4 and TRIB3, GDF15, TNFRSF10B in chondrocyte 5 may play an important role in this process.

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors.

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.