I. Nassiri, Andrew J Kwok, Aneesha Bhandari, Katherine R. Bull, Lucy C. Garner, Paul Klenerman, Caleb Webber, Laura Parkkinen, Angela W Lee, Yanxia Wu, Benjamin Fairfax, Julian C. Knight, David Buck, Paolo Piazza
{"title":"利用基因表达的个体间差异解复用单细胞 RNA 测序数据","authors":"I. Nassiri, Andrew J Kwok, Aneesha Bhandari, Katherine R. Bull, Lucy C. Garner, Paul Klenerman, Caleb Webber, Laura Parkkinen, Angela W Lee, Yanxia Wu, Benjamin Fairfax, Julian C. Knight, David Buck, Paolo Piazza","doi":"10.1093/bioadv/vbae085","DOIUrl":null,"url":null,"abstract":"\n \n \n Pooled designs for single-cell RNA sequencing, where many cells from distinct samples are processed jointly, offer increased throughput and reduced batch variation. This study describes expression-aware demultiplexing (EAD), a computational method that employs differential co-expression patterns between individuals to demultiplex pooled samples without any extra experimental steps.\n \n \n \n We use synthetic sample pools and show that the top interindividual differentially co-expressed genes provide a distinct cluster of cells per individual, significantly enriching the regulation of metabolism. Our application of EAD to samples of 6 isogenic inbred mice demonstrated that controlling genetic and environmental effects can solve inter-individual variations related to metabolic pathways. We utilized 30 samples from both sepsis and healthy individuals in six batches to assess the performance of classification approaches. The results indicate that combining genetic and EAD results can enhance the accuracy of assignments (Min 0.94, Mean 0.98, Max 1). The results were enhanced by an average of 1.4% when EAD and barcoding techniques were combined (Min. 1.25%, Median 1.33%, Max. 1.74%). Furthermore, we demonstrate that interindividual differential co-expression analysis within the same cell type can be used to identify cells from the same donor in different activation states. By analyzing single-nuclei transcriptome profiles from the brain, we demonstrate that our method can be applied to non-immune cells.\n \n \n \n Expression-aware demultiplexing workflow is available at https://isarnassiri.github.io/scDIV/ as an R package called scDIV (acronym for Single Cell RNA sequencing data Demultiplexing using Interindividual Variations).\n \n \n \n Supplementary data are available at Bioinformatics Advances online.\n","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":" 48","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Demultiplexing of Single-Cell RNA sequencing data using interindividual variation in gene expression\",\"authors\":\"I. Nassiri, Andrew J Kwok, Aneesha Bhandari, Katherine R. Bull, Lucy C. Garner, Paul Klenerman, Caleb Webber, Laura Parkkinen, Angela W Lee, Yanxia Wu, Benjamin Fairfax, Julian C. Knight, David Buck, Paolo Piazza\",\"doi\":\"10.1093/bioadv/vbae085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n \\n \\n Pooled designs for single-cell RNA sequencing, where many cells from distinct samples are processed jointly, offer increased throughput and reduced batch variation. This study describes expression-aware demultiplexing (EAD), a computational method that employs differential co-expression patterns between individuals to demultiplex pooled samples without any extra experimental steps.\\n \\n \\n \\n We use synthetic sample pools and show that the top interindividual differentially co-expressed genes provide a distinct cluster of cells per individual, significantly enriching the regulation of metabolism. Our application of EAD to samples of 6 isogenic inbred mice demonstrated that controlling genetic and environmental effects can solve inter-individual variations related to metabolic pathways. We utilized 30 samples from both sepsis and healthy individuals in six batches to assess the performance of classification approaches. The results indicate that combining genetic and EAD results can enhance the accuracy of assignments (Min 0.94, Mean 0.98, Max 1). The results were enhanced by an average of 1.4% when EAD and barcoding techniques were combined (Min. 1.25%, Median 1.33%, Max. 1.74%). Furthermore, we demonstrate that interindividual differential co-expression analysis within the same cell type can be used to identify cells from the same donor in different activation states. 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Demultiplexing of Single-Cell RNA sequencing data using interindividual variation in gene expression
Pooled designs for single-cell RNA sequencing, where many cells from distinct samples are processed jointly, offer increased throughput and reduced batch variation. This study describes expression-aware demultiplexing (EAD), a computational method that employs differential co-expression patterns between individuals to demultiplex pooled samples without any extra experimental steps.
We use synthetic sample pools and show that the top interindividual differentially co-expressed genes provide a distinct cluster of cells per individual, significantly enriching the regulation of metabolism. Our application of EAD to samples of 6 isogenic inbred mice demonstrated that controlling genetic and environmental effects can solve inter-individual variations related to metabolic pathways. We utilized 30 samples from both sepsis and healthy individuals in six batches to assess the performance of classification approaches. The results indicate that combining genetic and EAD results can enhance the accuracy of assignments (Min 0.94, Mean 0.98, Max 1). The results were enhanced by an average of 1.4% when EAD and barcoding techniques were combined (Min. 1.25%, Median 1.33%, Max. 1.74%). Furthermore, we demonstrate that interindividual differential co-expression analysis within the same cell type can be used to identify cells from the same donor in different activation states. By analyzing single-nuclei transcriptome profiles from the brain, we demonstrate that our method can be applied to non-immune cells.
Expression-aware demultiplexing workflow is available at https://isarnassiri.github.io/scDIV/ as an R package called scDIV (acronym for Single Cell RNA sequencing data Demultiplexing using Interindividual Variations).
Supplementary data are available at Bioinformatics Advances online.
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.