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Simple Summary: Despite recent advancements in experimental technology for genome-wide molecular profiling, our understanding of the dynamic mechanism underlying cancer drug resistance remains limited. In this study, we present an approach that combines mathematical modeling with the pseudotime analysis of single-cell time-series transcriptome data of drug-treated breast cancer cells. Our method identifies approximately 600 genes out of 6000 exhibiting multistable expression states, including RPS6KB1, a predictor of poor prognosis, cell survival, and growth in estrogen-receptor-positive breast cancers. The bifurcation analysis elucidates the regulatory mechanisms of the key regulatory genes, which can also be mapped into a molecular network based on cell survival and metastasis-related pathways, providing a comprehensive understanding of the interplay between signaling pathways and regulatory genes. Our method serves as a powerful tool for deciphering the complexities of drug resistance mechanisms in human diseases. Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560–680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer. [ABSTRACT FROM AUTHOR]

The epigenetic trajectory of DNA methylation profiles has a nonlinear relationship with time, reflecting rapid changes in DNA methylation early in life that progressively slow with age. In this study, we use pseudotime analysis to determine the functional form of these trajectories. Unlike epigenetic clocks that constrain the functional form of methylation changes with time, pseudotime analysis orders samples along a path, based on similarities in a latent dimension, to provide an unbiased trajectory. We show that pseudotime analysis can be applied to DNA methylation in human blood and brain tissue and find that it is highly correlated with the epigenetic states described by the Epigenetic Pacemaker. Moreover, we show that the pseudotime trajectory can be modeled with respect to time, using a sum of two exponentials, with coefficients that are close to the timescales of human age-associated mortality. Thus, for the first time, we can identify age-associated molecular changes that appear to track the exponential dynamics of mortality risk.

Human natural killer (NK) cells in lymphoid tissues can be categorized into three subsets: CD56brightCD16+, CD56dimCD16+ and CD69+CXCR6+ lymphoid tissue-resident (lt)NK cells. How the three subsets are functionally and developmentally related is currently unknown. Therefore, we performed single-cell RNA sequencing combined with oligonucleotide-conjugated antibodies against CD56, CXCR6, CD117 and CD34 on fresh bone marrow NK cells. A minor CD56dimGzmK+ subset was identified that shared features with CD56bright and CD56dimGzmK-NK cells based on transcriptome, phenotype (NKG2AhighCD16lowKLRG1highTIGIThigh) and functional analysis in bone marrow and blood, supportive for an intermediate subset. Pseudotime analysis positioned CD56bright, CD56dimGzmK+ and CD56dimGzmK- cells in one differentiation trajectory, while ltNK cells were developmentally separated. Integrative analysis with bone marrow cells from the Human Cell Atlas did not demonstrate a developmental connection between CD34+ progenitor and NK cells, suggesting absence of early NK cell stages in bone marrow. In conclusion, single-cell transcriptomics provide new insights on development and differentiation of human NK cells. [ABSTRACT FROM AUTHOR]

Introduction: Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment., Methods: Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI)., Results: Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI., Discussion: Our risk score holds great potential to improve the precision of early risk assessment., Highlights: Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Background: Glioblastoma (GBM) is an aggressive malignancy. The inherent resistance of GBM to radiotherapy poses great challenges for clinical treatment., Objectives: The primary objective of this study is to explore the molecular mechanisms of radiotherapy resistance in GBM and identify the key influencing factors that contribute to this phenomenon., Methods: The single-cell RNA sequencing (scRNA-seq) data of GBM were downloaded from the Gene Expression Omnibus (GEO) database. Cells were clustered using the Seurat R package, and the clusters were annotated using the CellMarker database. Pseudotime analysis was conducted using Monocle2. Marker scores were calculated based on the RNA-seq data of GBM from the UCSC database, and the enrichment of Hallmark gene sets was measured with the AUCell package. Furthermore, the most frequently mutated genes were identified using the simple nucleotide variation data from The Cancer Genome Atlas (TCGA) applying the maftools package., Results: This study identified two oligodendrocyte subsets (ODC3 and ODC4) as radiotherapy-resistant groups in GBM. Enrichment and Pseudotime analysis revealed that the inflammatory response and immune activation pathways were enriched in ODC3, while the cell division and interferon response pathways were enriched in ODC4. The enrichment scores of hallmark gene sets further confirmed that ODC3 and ODC4 subpopulations developed radiotherapy resistance via distinct molecular mechanisms. Analysis of gene mutation frequencies showed that TP53 exhibited the most significant change in mutation frequency, indicating that it was an important risk factor involved in radiotherapy resistance in GBM., Conclusion: We identified two ODC subpopulations that exhibited resistance to radiotherapy, providing a new perspective and potential targets for personalized treatment strategies for GBM., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Human natural killer (NK) cells in lymphoid tissues can be categorized into three subsets: CD56brightCD16+, CD56dimCD16+ and CD69+CXCR6+ lymphoid tissue-resident (lt)NK cells. How the three subsets are functionally and developmentally related is currently unknown. Therefore, we performed single-cell RNA sequencing combined with oligonucleotide-conjugated antibodies against CD56, CXCR6, CD117 and CD34 on fresh bone marrow NK cells. A minor CD56dimGzmK+ subset was identified that shared features with CD56bright and CD56dimGzmK- NK cells based on transcriptome, phenotype (NKG2AhighCD16lowKLRG1highTIGIThigh) and functional analysis in bone marrow and blood, supportive for an intermediate subset. Pseudotime analysis positioned CD56bright, CD56dimGzmK+ and CD56dimGzmK- cells in one differentiation trajectory, while ltNK cells were developmentally separated. Integrative analysis with bone marrow cells from the Human Cell Atlas did not demonstrate a developmental connection between CD34+ progenitor and NK cells, suggesting absence of early NK cell stages in bone marrow. In conclusion, single-cell transcriptomics provide new insights on development and differentiation of human NK cells.

Simple Summary: Although texture parameters of F-18 fluorodeoxyglucose positron emission tomography/computed tomography images were known to associate tumor biology and clinical features, the types and implications of parameters are too various and complicated. To overcome the limitation of texture parameter, we attempted to produce a new simplified parameter from texture parameters of F-18 fluorodeoxyglucose positron emission tomography/computed tomography images in lung cancer patients using pseudotime analysis. Pseudotime analysis is a recently developed method to explore changes in cell or tissue characteristics based on transcriptomic expression. It is the first study to apply pseudotime analysis into radiomics dataset other than transcriptomics data. Herein, we demonstrated that pseudotime can be successfully estimated from texture parameters. In the aspect of prognostic prediction, pseudotime was an independent prognostic factor for overall survival in contrast to conventional parameters such as metabolic tumor volume and total lesion glycolysis. This study showed possibility of integrating various texture parameters into single parameter which reflects disease progression status. Pseudotime, as a concrete value of disease progression, is expected to be used in clinical field to evaluate disease and predict prognosis. Texture analysis provides image parameters from F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT). Although some parameters are associated with tumor biology and clinical features, the types and implications of these parameters are complicated. We applied pseudotime analysis, which has recently been used to estimate changes in individual sample characteristics, to texture parameters from FDG PET/CT images of locally advanced non-small-cell lung cancer (NSCLC) patients undergoing neoadjuvant concurrent chemoradiation therapy (CCRT) followed by surgery. Our subjects were 303 NSCLC patients who underwent pretherapeutic FDG PET/CT and tri-modality therapy. Texture parameters of the primary tumor were calculated from FDG PET/CT images acquired before neoadjuvant CCRT. Pseudotime analysis was performed using the PhenoPath tool. Clinicopathologic features including survival data were collected and survival analysis was performed to compare the prognostic significances of pseudotime parameters with those of conventional PET parameters. Pseudotime was successfully estimated from texture parameters. Normalized co-occurrence homogeneity, normalized co-occurrence inverse difference moment, and black–white symmetry showed positive correlations with pseudotime, short run emphasis, normalized co-occurrence dissimilarity, and short zone emphasis negative correlation. The maximum standardized uptake value (SUV) and mean SUV were not associated with overall survival. Pseudotime, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) showed significant associations with overall survival. In contrast to MTV and TLG, pseudotime was an independent prognostic factor for overall survival. Various metabolic texture parameters can be integrated into a single parameter using pseudotime analysis. Pseudotime of the primary tumor, estimated from FDG PET/CT images, better predicts overall survival in locally advanced NSCLC patients treated with tri-modality therapy than conventional PET parameters. [ABSTRACT FROM AUTHOR]

Introduction: As the molecular features of lung adenocarcinoma (LUAD) have been evaluated as a cross-sectional study, the course of tumor characteristics has not been modeled. The temporal evolution of the tumor immune microenvironment (TIME), as well as the clinico-molecular features of LUAD, could provide a precise strategy for immunotherapy and surrogate biomarkers for the course of LUAD. Methods: A pseudotime trajectory was constructed in patients with LUAD from the Cancer Genome Atlas and non-small cell lung cancer radiogenomics datasets. Correlation analyses were performed between clinical features and pseudotime. Genes associated with pseudotime were selected, and gene ontology analysis was performed. F-18 fluorodeoxyglucose positron emission tomography images of subjects were collected, and imaging parameters, including standardized uptake value (SUV), were obtained. Correlation analyses were performed between imaging parameters and pseudotime. Correlation analyses were performed between the enrichment scores of various immune cell types and pseudotime. In addition, correlation analyses were performed between the expression of PD-L1, tumor mutation burden, and pseudotime. Results: Pseudotime trajectories of LUAD corresponded to clinical stages. Molecular profiles related to cell division and natural killer cell activity were changed along the pseudotime. The maximal SUV of LUAD tumors showed a positive correlation with pseudotime. Type 1 helper T (Th1) cells showed a positive correlation, whereas M2 macrophages showed a negative correlation with pseudotime. PD-L1 expression showed a negative correlation, whereas tumor mutation burden showed a positive correlation with pseudotime. Conclusion: The estimated pseudotime associated with the stage suggested that it could reflect the clinico-molecular evolution of LUAD. Specific immune cell types in the TIME as well as cell division and glucose metabolism were dynamically changed according to the progression of the pseudotime. As a molecular progression of LUAD, different cellular targets should be considered for immunotherapy. [ABSTRACT FROM AUTHOR]

IntroductionAs the molecular features of lung adenocarcinoma (LUAD) have been evaluated as a cross-sectional study, the course of tumor characteristics has not been modeled. The temporal evolution of the tumor immune microenvironment (TIME), as well as the clinico-molecular features of LUAD, could provide a precise strategy for immunotherapy and surrogate biomarkers for the course of LUAD.MethodsA pseudotime trajectory was constructed in patients with LUAD from the Cancer Genome Atlas and non-small cell lung cancer radiogenomics datasets. Correlation analyses were performed between clinical features and pseudotime. Genes associated with pseudotime were selected, and gene ontology analysis was performed. F-18 fluorodeoxyglucose positron emission tomography images of subjects were collected, and imaging parameters, including standardized uptake value (SUV), were obtained. Correlation analyses were performed between imaging parameters and pseudotime. Correlation analyses were performed between the enrichment scores of various immune cell types and pseudotime. In addition, correlation analyses were performed between the expression of PD-L1, tumor mutation burden, and pseudotime.ResultsPseudotime trajectories of LUAD corresponded to clinical stages. Molecular profiles related to cell division and natural killer cell activity were changed along the pseudotime. The maximal SUV of LUAD tumors showed a positive correlation with pseudotime. Type 1 helper T (Th1) cells showed a positive correlation, whereas M2 macrophages showed a negative correlation with pseudotime. PD-L1 expression showed a negative correlation, whereas tumor mutation burden showed a positive correlation with pseudotime.ConclusionThe estimated pseudotime associated with the stage suggested that it could reflect the clinico-molecular evolution of LUAD. Specific immune cell types in the TIME as well as cell division and glucose metabolism were dynamically changed according to the progression of the pseudotime. As a molecular progression of LUAD, different cellular targets should be considered for immunotherapy.

Objectives: The aim of the present study was to construct a polygenic risk score (PRS) for poor survival among patients with stomach adenocarcinoma (STAD) based on expression of malignant cell markers.Methods: Integrated analyses of bulk and single-cell RNA sequencing (scRNA-seq) of STAD and normal stomach tissues were conducted to identify malignant and non-malignant markers. Analyses of the scRNA-seq profile from early STAD were used to explore intratumoral heterogeneity (ITH) of the malignant cell subpopulations. Dimension reduction, cell clustering, pseudotime, and gene set enrichment analyses were performed. The marker genes of each malignant tissue and cell clusters were screened to create a PRS using Cox regression analyses. Combined with the PRS and routine clinicopathological characteristics, a nomogram tool was generated to predict prognosis of patients with STAD. The prognostic power of the PRS was validated in two independent external datasets.Results: The malignant and non-malignant cells were identified according to 50 malignant and non-malignant cell markers. The malignant cells were divided into nine clusters with different marker genes and biological characteristics. Pseudotime analysis showed the potential differentiation trajectory of these nine malignant cell clusters and identified genes that affect cell differentiation. Ten malignant cell markers were selected to generate a PRS: RGS1, AADAC, NPC2, COL10A1, PRKCSH, RAMP1, PRR15L, TUBA1A, CXCR6, and UPP1. The PRS was associated with both overall and progression-free survival (PFS) and proved to be a prognostic factor independent of routine clinicopathological characteristics. PRS could successfully divide patients with STAD in three datasets into high- or low-risk groups. In addition, we combined PRS and the tumor clinicopathological characteristics into a nomogram tool to help predict the survival of patients with STAD.Conclusion: We revealed limited but significant intratumoral heterogeneity in STAD and proposed a malignant cell subset marker-based PRS through integrated analysis of bulk sequencing and scRNA-seq data.

Background: Epithelial-mesenchymal transition (EMT) has been recognized as playing a crucial role in cancer progression. Among the studies on EMT, biomarker detection has been one of the important topics to understand the biology and mechanism of EMT related to tumor progression and treatment resistance. The existing methods often identified differentially-expressed genes as potential markers by ranking all genes by their variances. This paper proposes a novel method to detect markers for respective lineages in the EMT process. Methods and Results: Our method consists of three steps: first, perform trajectory inference to identify the lineage of transitional processes in EMT progression, and secondly, identify the lineage for EMT reversion in addition to EMT progression, and thirdly detect biomarkers for both of the EMT progression and reversion lineages with differential expression analysis. Furthermore, to elucidate the heterogeneity of the EMT process, we performed a clustering analysis of the cells in the EMT progression and reversion conditions. We then explored branching trajectories that order clusters using time information of the time-course samples. Using this method, we successfully detected two potential biomarkers related to EMT, phospholipid phosphatase 4 (PLPP4) and lymphotoxin-beta (LTB), which have not been detected by the existing method. Conclusions: In this study, we propose a method for the detection of biomarkers of EMT based on trajectory inference with single-cell RNA-seq data. The performance of the method is demonstrated by the detection of potential biomarkers related to EMT.

The epigenetic trajectory of DNA methylation profiles has a nonlinear relationship with time, reflecting rapid changes in DNA methylation early in life that progressively slow with age. In this study, we use pseudotime analysis to determine the functional form of these trajectories. Unlike epigenetic clocks that constrain the functional form of methylation changes with time, pseudotime analysis orders samples along a path, based on similarities in a latent dimension, to provide an unbiased trajectory. We show that pseudotime analysis can be applied to DNA methylation in human blood and brain tissue and find that it is highly correlated with the epigenetic states described by the Epigenetic Pacemaker. Moreover, we show that the pseudotime trajectory can be modeled with respect to time, using a sum of two exponentials, with coefficients that are close to the timescales of human age-associated mortality. Thus, for the first time, we can identify age-associated molecular changes that appear to track the exponential dynamics of mortality risk.

The mechanisms shaping the amino acids recruitment pattern into the proteins in the early life history presently remains a huge mystery. In this study, we conducted genome-wide analyses of amino acids usage and genetic codons structure in 7270 species across three domains of life. The carried-out analyses evidenced ubiquitous usage bias of amino acids that were likely independent from codon usage bias. Taking advantage of codon usage bias, we performed pseudotime analysis to re-determine the chronological order of the species emergence, which inspired a new species relationship by tracing the imprint of codon usage evolution. Furthermore, the multidimensional data integration showed that the amino acids A, D, E, G, L, P, R, S, T and V might be the first recruited into the last universal common ancestry (LUCA) proteins. The data analysis also indicated that the remaining amino acids most probably were gradually incorporated into proteogenesis process in the course of two long-timescale parallel evolutionary routes: I→F→Y→C→M→W and K→N→Q→H. This study provides new insight into the origin of life, particularly in terms of the basic protein composition of early life. Our work provides crucial information that will help in a further understanding of protein structure and function in relation to their evolutionary history.

The significance of necroptosis in recurrent or metastatic cervical cancer remains unclear. In this study, we utilized various bioinformatics methods to analyze the cancer genome atlas (TCGA) data, gene chip and the single-cell RNA-sequencing (scRNA seq) data. And a necroptosis-related genes signature for prognostic assessment of patients with cervical cancer was constructed successfully. Survival analysis, receiver operating characteristic (ROC) curve, the support vector machine recursive feature elimination (SVM-RFE) algorithm and random forest analysis were performed to validate this signature. Patients in TCGA-CESC cohort were grouped into "high-necroptosis score (H-NCPS)" vs "low-necroptosis score (L-NCPS)" subgroups based on the median of necroptosis score of each patient. Analyses of the tumor microenvironment manifested "H-NCPS" patients associated with lower degree of immune infiltration. Through the utilization of survival analysis, cell communication, and Gene Set Enrichment Analysis (GSEA), PGK1 was determined to be the pivotal gene within the 9-gene signature associated with necroptosis. The high expression of PGK1 in cervical cancer cells was confirmed through the utilization of quantitative real-time polymerase chain reaction (RT-qPCR) and the human protein atlas (HPA). In the interim, PGK1 prompted the transition of M1 macrophages to M2 macrophages and influenced the occurrence and development of necroptosis. In conclusion, the 9-gene signature developed from necroptosis-related genes has shown significant predictive capabilities for the prognosis of cervical cancer, offered valuable guidance for individualized and targeted treatment approaches for patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Macrophages play an important role in the occurrence and development of atherosclerosis. However, few existing studies have deliberately analyzed the changes in characteristic genes in the process of macrophage phenotype transformation., Method: Carotid atherosclerotic plaque single-cell RNA (scRNA) sequencing data were analyzed to define the cells involved and determine their transcriptomic characteristics. KEGG enrichment analysis, CIBERSORT, ESTIMATE, support vector machine (SVM), random forest (RF), and weighted correlation network analysis (WGCNA) were applied to bulk sequencing data. All data were downloaded from Gene Expression Omnibus (GEO)., Result: Nine cell clusters were identified. M1 macrophages, M2 macrophages, and M2/M1 macrophages were identified as three clusters within the macrophages. According to pseudotime analysis, M2/M1 macrophages and M2 macrophages can be transformed into M1 macrophages. The ROC curve values of the six genes in the test group were statistically significant (AUC (IL1RN): 0.899, 95% CI: 0.764-0.990; AUC (NRP1): 0.817, 95% CI: 0.620-0.971; AUC (TAGLN): 0.846, 95% CI: 0.678-0.971; AUC (SPARCL1): 0.825, 95% CI: 0.620-0.988; AUC (EMP2): 0.808, 95% CI: 0.630-0.947; AUC (ACTA2): 0.784, 95% CI: 0.591-0.938). The atherosclerosis prediction model showed significant statistical significance in both the train group (AUC: 0.909, 95% CI: 0.842-0.967) and the test group (AUC: 0.812, 95% CI: 0.630-0.966)., Conclusions: IL1RN High M1, NRP1 High M2, ACTA2 High M2/M1, EMP2 High M1/M1, SPACL1 High M2/M1 and TAGLN High M2/M1 macrophages play key roles in the occurrence and development of arterial atherosclerosis. These marker genes of macrophage phenotypic transformation can also be used to establish a model to predict the occurrence of atherosclerosis.

Human natural killer (NK) cells in lymphoid tissues can be categorized into three subsets: CD56 bright CD16 + , CD56 dim CD16 + and CD69 + CXCR6 + lymphoid tissue-resident (lt)NK cells. How the three subsets are functionally and developmentally related is currently unknown. Therefore, we performed single-cell RNA sequencing combined with oligonucleotide-conjugated antibodies against CD56, CXCR6, CD117 and CD34 on fresh bone marrow NK cells. A minor CD56 dim GzmK + subset was identified that shared features with CD56 bright and CD56 dim GzmK - NK cells based on transcriptome, phenotype (NKG2A high CD16 low KLRG1 high TIGIT high ) and functional analysis in bone marrow and blood, supportive for an intermediate subset. Pseudotime analysis positioned CD56 bright , CD56 dim GzmK + and CD56 dim GzmK - cells in one differentiation trajectory, while ltNK cells were developmentally separated. Integrative analysis with bone marrow cells from the Human Cell Atlas did not demonstrate a developmental connection between CD34 + progenitor and NK cells, suggesting absence of early NK cell stages in bone marrow. In conclusion, single-cell transcriptomics provide new insights on development and differentiation of human NK cells., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Melsen, van Ostaijen-ten Dam, Schoorl, Schol, van den Homberg, Lankester, Lugthart and Schilham.)

Loss of sensory hair cells (HCs) in the mammalian inner ear leads to permanent hearing and vestibular defects, whereas loss of HCs in zebrafish results in their regeneration. We used single-cell RNA sequencing (scRNA-seq) to characterize the transcriptional dynamics of HC regeneration in zebrafish at unprecedented spatiotemporal resolution. We uncovered three sequentially activated modules: first, an injury/inflammatory response and downregulation of progenitor cell maintenance genes within minutes after HC loss; second, the transient activation of regeneration-specific genes; and third, a robust re-activation of developmental gene programs, including HC specification, cell-cycle activation, ribosome biogenesis, and a metabolic switch to oxidative phosphorylation. The results are relevant not only for our understanding of HC regeneration and how we might be able to trigger it in mammals but also for regenerative processes in general. The data are searchable and publicly accessible via a web-based interface. [Display omitted] • An scRNA-seq atlas of hair cell regeneration at unprecedented spatiotemporal resolution • Regeneration occurs via three sequentially activated gene modules • Trajectory analyses show that progenitors can acquire one of two fates • The data can be queried using publicly available databases It is not understood why mammals cannot regenerate their inner ear hair cells. Baek et al. performed single-cell RNA sequencing (scRNA-seq) analyses of six time points during sensory hair cell regeneration in zebrafish. Regeneration depends on three subsequently activated gene modules that can serve as a blueprint to trigger regeneration in mammals. [ABSTRACT FROM AUTHOR]

The mechanisms shaping the amino acids recruitment pattern into the proteins in the early life history presently remains a huge mystery. In this study, we conducted genome-wide analyses of amino acids usage and genetic codons structure in 7270 species across three domains of life. The carried-out analyses evidenced ubiquitous usage bias of amino acids that were likely independent from codon usage bias. Taking advantage of codon usage bias, we performed pseudotime analysis to re-determine the chronological order of the species emergence, which inspired a new species relationship by tracing the imprint of codon usage evolution. Furthermore, the multidimensional data integration showed that the amino acids A, D, E, G, L, P, R, S, T and V might be the first recruited into the last universal common ancestry (LUCA) proteins. The data analysis also indicated that the remaining amino acids most probably were gradually incorporated into proteogenesis process in the course of two long-timescale parallel evolutionary routes: I→F→Y→C→M→W and K→N→Q→H. This study provides new insight into the origin of life, particularly in terms of the basic protein composition of early life. Our work provides crucial information that will help in a further understanding of protein structure and function in relation to their evolutionary history. [ABSTRACT FROM AUTHOR]

Trajectory inference (TI) or pseudotime analysis has dramatically extended the analytical framework of single-cell RNA-seq data, allowing regulatory genes contributing to cell differentiation and those involved in various dynamic cellular processes to be identified. However, most TI analysis procedures deal with individual genes independently while overlooking the regulatory relations between genes. Integrating information from gene regulatory networks (GRNs) at different pseudotime points may lead to more interpretable TI results. To this end, we introduce scInTime—an unsupervised machine learning framework coupling inferred trajectory with single-cell GRNs (scGRNs) to identify master regulatory genes. We validated the performance of our method by analyzing multiple scRNA-seq data sets. In each of the cases, top-ranking genes predicted by scInTime supported their functional relevance with corresponding signaling pathways, in line with the results of available functional studies. Overall results demonstrated that scInTime is a powerful tool to exploit pseudotime-series scGRNs, allowing for a clear interpretation of TI results toward more significant biological insights. [ABSTRACT FROM AUTHOR]

Background: Epithelial-mesenchymal transition (EMT) has been recognized as playing a crucial role in cancer progression. Among the studies on EMT, biomarker detection has been one of the important topics to understand the biology and mechanism of EMT related to tumor progression and treatment resistance. The existing methods often identified differentially-expressed genes as potential markers by ranking all genes by their variances. This paper proposes a novel method to detect markers for respective lineages in the EMT process., Methods and Results: Our method consists of three steps: first, perform trajectory inference to identify the lineage of transitional processes in EMT progression, and secondly, identify the lineage for EMT reversion in addition to EMT progression, and thirdly detect biomarkers for both of the EMT progression and reversion lineages with differential expression analysis. Furthermore, to elucidate the heterogeneity of the EMT process, we performed a clustering analysis of the cells in the EMT progression and reversion conditions. We then explored branching trajectories that order clusters using time information of the time-course samples. Using this method, we successfully detected two potential biomarkers related to EMT, phospholipid phosphatase 4 (PLPP4) and lymphotoxin-beta (LTB), which have not been detected by the existing method., Conclusions: In this study, we propose a method for the detection of biomarkers of EMT based on trajectory inference with single-cell RNA-seq data. The performance of the method is demonstrated by the detection of potential biomarkers related to EMT., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s). Published by IMR Press.)

Objectives: The aim of the present study was to construct a polygenic risk score (PRS) for poor survival among patients with stomach adenocarcinoma (STAD) based on expression of malignant cell markers. Methods: Integrated analyses of bulk and single-cell RNA sequencing (scRNA-seq) of STAD and normal stomach tissues were conducted to identify malignant and non-malignant markers. Analyses of the scRNA-seq profile from early STAD were used to explore intratumoral heterogeneity (ITH) of the malignant cell subpopulations. Dimension reduction, cell clustering, pseudotime, and gene set enrichment analyses were performed. The marker genes of each malignant tissue and cell clusters were screened to create a PRS using Cox regression analyses. Combined with the PRS and routine clinicopathological characteristics, a nomogram tool was generated to predict prognosis of patients with STAD. The prognostic power of the PRS was validated in two independent external datasets. Results: The malignant and non-malignant cells were identified according to 50 malignant and non-malignant cell markers. The malignant cells were divided into nine clusters with different marker genes and biological characteristics. Pseudotime analysis showed the potential differentiation trajectory of these nine malignant cell clusters and identified genes that affect cell differentiation. Ten malignant cell markers were selected to generate a PRS: RGS1, AADAC, NPC2, COL10A1, PRKCSH, RAMP1, PRR15L, TUBA1A, CXCR6, and UPP1. The PRS was associated with both overall and progression-free survival (PFS) and proved to be a prognostic factor independent of routine clinicopathological characteristics. PRS could successfully divide patients with STAD in three datasets into high- or low-risk groups. In addition, we combined PRS and the tumor clinicopathological characteristics into a nomogram tool to help predict the survival of patients with STAD. Conclusion: We revealed limited but significant intratumoral heterogeneity in STAD and proposed a malignant cell subset marker-based PRS through integrated analysis of bulk sequencing and scRNA-seq data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zou, Lv, Gan, Liao and Liang.)

Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches. [ABSTRACT FROM AUTHOR]