Dataset Information

Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape.

ABSTRACT: Genome-wide identification of single-cell transcriptomic responses of drugs in various human cells is a challenging issue in medical and pharmaceutical research. Here we present a computational method, tensor-based imputation of gene-expression data at the single-cell level (TIGERS), which reveals the drug-induced single-cell transcriptomic landscape. With this algorithm, we predict missing drug-induced single-cell gene-expression data with tensor imputation, and identify trajectories of regulated pathways considering intercellular heterogeneity. Tensor imputation outperformed existing imputation methods for data completion, and provided cell-type-specific transcriptomic responses for unobserved drugs. For example, TIGERS correctly predicted the cell-type-specific expression of maker genes for pancreatic islets. Pathway trajectory analysis of the imputed gene-expression profiles of all combinations of drugs and human cells identified single-cell-specific drug activities and pathway trajectories that reflect drug-induced changes in pathway regulation. The proposed method is expected to expand our understanding of the single-cell mechanisms of drugs at the pathway level.

SUBMITTER: Iwata M

PROVIDER: S-EPMC10768635 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape.

Iwata Michio M Mutsumine Hiroaki H Nakayama Yusuke Y Suita Naomasa N Yamanishi Yoshihiro Y

Nature computational science 20221124 11

Genome-wide identification of single-cell transcriptomic responses of drugs in various human cells is a challenging issue in medical and pharmaceutical research. Here we present a computational method, tensor-based imputation of gene-expression data at the single-cell level (TIGERS), which reveals the drug-induced single-cell transcriptomic landscape. With this algorithm, we predict missing drug-induced single-cell gene-expression data with tensor imputation, and identify trajectories of regulat ...[more]

PMID: 38177364

Similar Datasets

Project description:Study questionWhat is the molecular landscape underlying the functional decline of human testicular ageing?Summary answerThe present study provides a comprehensive single-cell transcriptomic atlas of testes from young and old humans and offers insights into the molecular mechanisms and potential targets for human testicular ageing.What is known alreadyTesticular ageing is known to cause male age-related fertility decline and hypogonadism. Dysfunction of testicular cells has been considered as a key factor for testicular ageing.Study design, size, durationHuman testicular biopsies were collected from three young individuals and three old individuals to perform single-cell RNA sequencing (scRNA-seq). The key results were validated in a larger cohort containing human testicular samples from 10 young donors and 10 old donors.Participants/materials, setting, methodsscRNA-seq was used to identify gene expression signatures for human testicular cells during ageing. Ageing-associated changes of gene expression in spermatogonial stem cells (SSCs) and Leydig cells (LCs) were analysed by gene set enrichment analysis and validated by immunofluorescent and functional assays. Cell-cell communication analysis was performed using CellChat.Main results and the role of chanceThe single-cell transcriptomic landscape of testes from young and old men was surveyed, revealing age-related changes in germline and somatic niche cells. In-depth evaluation of the gene expression dynamics in germ cells revealed that the disruption of the base-excision repair pathway is a prominent characteristic of old SSCs, suggesting that defective DNA repair in SSCs may serve as a potential driver for increased de novo germline mutations with age. Further analysis of ageing-associated transcriptional changes demonstrated that stress-related changes and cytokine pathways accumulate in old somatic cells. Age-related impairment of redox homeostasis in old LCs was identified and pharmacological treatment with antioxidants alleviated this cellular dysfunction of LCs and promoted testosterone production. Lastly, our results revealed that decreased pleiotrophin signalling was a contributing factor for impaired spermatogenesis in testicular ageing.Large scale dataThe scRNA-seq sequencing and processed data reported in this paper were deposited at the Genome Sequence Archive (https://ngdc.cncb.ac.cn/), under the accession number HRA002349.Limitations, reasons for cautionOwing to the difficulty in collecting human testis tissue, the sample size was limited. Further in-depth functional and mechanistic studies are warranted in future.Wider implications of the findingsThese findings provide a comprehensive understanding of the cell type-specific mechanisms underlying human testicular ageing at a single-cell resolution, and suggest potential therapeutic targets that may be leveraged to address age-related male fertility decline and hypogonadism.Study funding/competing interest(s)This work was supported by the National Key Research and Development Program of China (2022YFA1104100), the National Natural Science Foundation of China (32130046, 82171564, 82101669, 82371611, 82371609, 82301796), the Natural Science Foundation of Guangdong Province, China (2022A1515010371), the Major Project of Medical Science and Technology Development Research Center of National Health Planning Commission, China (HDSL202001000), the Open Project of NHC Key Laboratory of Male Reproduction and Genetics (KF202001), the Guangdong Province Regional Joint Fund-Youth Fund Project (2021A1515110921, 2022A1515111201), and the China Postdoctoral Science Foundation (2021M703736). The authors declare no conflict of interest.

Project description:ObjectiveNeuroinflammation is associated with brain injury and poor outcomes after aneurysmal subarachnoid hemorrhage (SAH). In this study, we performed single-cell RNA sequencing (scRNA-seq) to analyze monocytes and explore the mechanisms of neuroinflammation after SAH.MethodsWe recruited two male patients with SAH and collected paired cerebrospinal fluid (CSF) and peripheral blood (PB) samples from each patient. Mononuclear cells from the CSF and PB samples were sequenced using 10x Genomics scRNA-seq. Additionally, scRNA-seq data for CSF from eight healthy individuals were obtained from the Gene Expression Omnibus database, serving as healthy controls (HC). We employed various R packages to comprehensively study the heterogeneity of transcriptome and phenotype of monocytes, including monocyte subset identification, function pathways, development and differentiation, and communication interaction.Results(1) A total of 17,242 cells were obtained in this study, including 7,224 cells from CSF and 10,018 cells from PB, mainly identified as monocytes, T cells, B cells, and NK cells. (2) Monocytes were divided into three subsets based on the expression of CD14 and CD16: classical monocytes (CM), intermediate monocytes (IM), and nonclassical monocytes (NCM). Differentially expressed gene modules regulated the differentiation and biological function in monocyte subsets. (3) Compared with healthy controls, both the toll-like receptor (TLR) and nod-like receptor (NLR) pathways were significantly activated and upregulated in IM from CSF after SAH. The biological processes related to neuroinflammation, such as leukocyte migration and immune response regulation, were also enriched in IM. These findings revealed that IM may play a key role in neuroinflammation by mediating the TLR and NLR pathways after SAH.InterpretationIn conclusion, we establish a single-cell transcriptomic landscape of immune cells and uncover the heterogeneity of monocyte subsets in SAH. These findings offer new insights into the underlying mechanisms of neuroinflammation and therapeutic targets for SAH.

Project description:Background: Single-cell RNA-seq is a powerful tool for measuring gene expression at the resolution of individual cells. A challenge in the analysis of this data is the large amount of zero values, representing either missing data or no expression. Several imputation approaches have been proposed to address this issue, but they generally rely on structure inherent to the dataset under consideration they may not provide any additional information, hence, are limited by the information contained therein and the validity of their assumptions. Methods: We evaluated the risk of generating false positive or irreproducible differential expression when imputing data with six different methods. We applied each method to a variety of simulated datasets as well as to permuted real single-cell RNA-seq datasets and consider the number of false positive gene-gene correlations and differentially expressed genes. Using matched 10X and Smart-seq2 data we examined whether cell-type specific markers were reproducible across datasets derived from the same tissue before and after imputation. Results: The extent of false-positives introduced by imputation varied considerably by method. Data smoothing based methods, MAGIC, knn-smooth and dca, generated many false-positives in both real and simulated data. Model-based imputation methods typically generated fewer false-positives but this varied greatly depending on the diversity of cell-types in the sample. All imputation methods decreased the reproducibility of cell-type specific markers, although this could be mitigated by selecting markers with large effect size and significance. Conclusions: Imputation of single-cell RNA-seq data introduces circularity that can generate false-positive results. Thus, statistical tests applied to imputed data should be treated with care. Additional filtering by effect size can reduce but not fully eliminate these effects. Of the methods we considered, SAVER was the least likely to generate false or irreproducible results, thus should be favoured over alternatives if imputation is necessary.

Dataset Information

Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape.

Publications

Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets