1
|
Nassiri I, Kwok AJ, Bhandari A, Bull KR, Garner LC, Klenerman P, Webber C, Parkkinen L, Lee AW, Wu Y, Fairfax B, Knight JC, Buck D, Piazza P. Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression. BIOINFORMATICS ADVANCES 2024; 4:vbae085. [PMID: 38911824 PMCID: PMC11193101 DOI: 10.1093/bioadv/vbae085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Accepted: 06/07/2024] [Indexed: 06/25/2024]
Abstract
Motivation 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. Results 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 six isogenic inbred mice demonstrated that controlling genetic and environmental effects can solve interindividual 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 analysing single-nuclei transcriptome profiles from the brain, we demonstrate that our method can be applied to nonimmune cells. Availability and implementation EAD 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).
Collapse
Affiliation(s)
- Isar Nassiri
- Nuffield Department of Medicine, Centre for Human Genetics, Oxford-GSK Institute of Molecular and Computational Medicine (IMCM), University of Oxford, Oxford, OX3 7BN, United Kingdom
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Andrew J Kwok
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, 999077, China
| | - Aneesha Bhandari
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Katherine R Bull
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Lucy C Garner
- Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Paul Klenerman
- Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 9DU, United Kingdom
- Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, OX1 3SY, United Kingdom
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom
| | - Caleb Webber
- Department of Physiology, Anatomy, Genetics, Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, OX1 3PT, United Kingdom
- UK Dementia Research Institute, Cardiff University, Cardiff, CF24 4HQ, United Kingdom
| | - Laura Parkkinen
- Nuffield Department of Medicine, Centre for Human Genetics, Oxford-GSK Institute of Molecular and Computational Medicine (IMCM), University of Oxford, Oxford, OX3 7BN, United Kingdom
- Nuffield Department of Clinical Neurosciences, Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Angela W Lee
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Yanxia Wu
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Benjamin Fairfax
- MRC–Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, OX3 9DS, United Kingdom
- Department of Oncology, University of Oxford & Oxford Cancer Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7DQ, United Kingdom
| | - Julian C Knight
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
- Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - David Buck
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Paolo Piazza
- Nuffield Department of Medicine, Centre for Human Genetics, Oxford-GSK Institute of Molecular and Computational Medicine (IMCM), University of Oxford, Oxford, OX3 7BN, United Kingdom
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| |
Collapse
|
2
|
Li L, Sun J, Fu Y, Changrob S, McGrath JJC, Wilson PC. A hybrid demultiplexing strategy that improves performance and robustness of cell hashing. Brief Bioinform 2024; 25:bbae254. [PMID: 38828640 PMCID: PMC11145454 DOI: 10.1093/bib/bbae254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 04/27/2024] [Accepted: 05/08/2024] [Indexed: 06/05/2024] Open
Abstract
Cell hashing, a nucleotide barcode-based method that allows users to pool multiple samples and demultiplex in downstream analysis, has gained widespread popularity in single-cell sequencing due to its compatibility, simplicity, and cost-effectiveness. Despite these advantages, the performance of this method remains unsatisfactory under certain circumstances, especially in experiments that have imbalanced sample sizes or use many hashtag antibodies. Here, we introduce a hybrid demultiplexing strategy that increases accuracy and cell recovery in multi-sample single-cell experiments. This approach correlates the results of cell hashing and genetic variant clustering, enabling precise and efficient cell identity determination without additional experimental costs or efforts. In addition, we developed HTOreader, a demultiplexing tool for cell hashing that improves the accuracy of cut-off calling by avoiding the dominance of negative signals in experiments with many hashtags or imbalanced sample sizes. When compared to existing methods using real-world datasets, this hybrid approach and HTOreader consistently generate reliable results with increased accuracy and cell recovery.
Collapse
Affiliation(s)
- Lei Li
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, United States
| | - Jiayi Sun
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
| | - Yanbin Fu
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
| | - Siriruk Changrob
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
| | - Joshua J C McGrath
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
| | - Patrick C Wilson
- Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, 413 E. 69th Street, New York, NY 10021, United States
| |
Collapse
|
3
|
Curion F, Wu X, Heumos L, André MMG, Halle L, Ozols M, Grant-Peters M, Rich-Griffin C, Yeung HY, Dendrou CA, Schiller HB, Theis FJ. hadge: a comprehensive pipeline for donor deconvolution in single-cell studies. Genome Biol 2024; 25:109. [PMID: 38671451 PMCID: PMC11055383 DOI: 10.1186/s13059-024-03249-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Single-cell multiplexing techniques (cell hashing and genetic multiplexing) combine multiple samples, optimizing sample processing and reducing costs. Cell hashing conjugates antibody-tags or chemical-oligonucleotides to cell membranes, while genetic multiplexing allows to mix genetically diverse samples and relies on aggregation of RNA reads at known genomic coordinates. We develop hadge (hashing deconvolution combined with genotype information), a Nextflow pipeline that combines 12 methods to perform both hashing- and genotype-based deconvolution. We propose a joint deconvolution strategy combining best-performing methods and demonstrate how this approach leads to the recovery of previously discarded cells in a nuclei hashing of fresh-frozen brain tissue.
Collapse
Affiliation(s)
- Fabiola Curion
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Xichen Wu
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Lukas Heumos
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
- Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Mylene Mariana Gonzales André
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Lennard Halle
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
| | - Matiss Ozols
- Wellcome Sanger Institute, Hinxton, UK
- School of Cell Matrix and Regenerative Medicine, The University of Manchester, Manchester, UK
| | - Melissa Grant-Peters
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Charlotte Rich-Griffin
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Hing-Yuen Yeung
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Calliope A Dendrou
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, The Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Herbert B Schiller
- Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
- Research Unit Precision Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
- Institute of Experimental Pneumology, LMU University Hospital, Ludwig-Maximilians University, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
| |
Collapse
|
4
|
Brown DV, Anttila CJA, Ling L, Grave P, Baldwin TM, Munnings R, Farchione AJ, Bryant VL, Dunstone A, Biben C, Taoudi S, Weber TS, Naik SH, Hadla A, Barker HE, Vandenberg CJ, Dall G, Scott CL, Moore Z, Whittle JR, Freytag S, Best SA, Papenfuss AT, Olechnowicz SWZ, MacRaild SE, Wilcox S, Hickey PF, Amann-Zalcenstein D, Bowden R. A risk-reward examination of sample multiplexing reagents for single cell RNA-Seq. Genomics 2024; 116:110793. [PMID: 38220132 DOI: 10.1016/j.ygeno.2024.110793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 11/29/2023] [Accepted: 01/09/2024] [Indexed: 01/16/2024]
Abstract
Single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for understanding cellular heterogeneity and function. However the choice of sample multiplexing reagents can impact data quality and experimental outcomes. In this study, we compared various multiplexing reagents, including MULTI-Seq, Hashtag antibody, and CellPlex, across diverse sample types such as human peripheral blood mononuclear cells (PBMCs), mouse embryonic brain and patient-derived xenografts (PDXs). We found that all multiplexing reagents worked well in cell types robust to ex vivo manipulation but suffered from signal-to-noise issues in more delicate sample types. We compared multiple demultiplexing algorithms which differed in performance depending on data quality. We find that minor improvements to laboratory workflows such as titration and rapid processing are critical to optimal performance. We also compared the performance of fixed scRNA-Seq kits and highlight the advantages of the Parse Biosciences kit for fragile samples. Highly multiplexed scRNA-Seq experiments require more sequencing resources, therefore we evaluated CRISPR-based destruction of non-informative genes to enhance sequencing value. Our comprehensive analysis provides insights into the selection of appropriate sample multiplexing reagents and protocols for scRNA-Seq experiments, facilitating more accurate and cost-effective studies.
Collapse
Affiliation(s)
- Daniel V Brown
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia.
| | - Casey J A Anttila
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia
| | - Ling Ling
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia
| | - Patrick Grave
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia
| | - Tracey M Baldwin
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia
| | - Ryan Munnings
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Anthony J Farchione
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Vanessa L Bryant
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia; The Royal Melbourne Hospital, 300 Grattan St, Parkville, Melbourne 3010, VIC, Australia
| | - Amelia Dunstone
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia
| | - Christine Biben
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Samir Taoudi
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Tom S Weber
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Shalin H Naik
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Anthony Hadla
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Holly E Barker
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Cassandra J Vandenberg
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Genevieve Dall
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Clare L Scott
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Zachery Moore
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - James R Whittle
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia; Peter MacCallum Cancer Centre, 305 Grattan St, Parkville, Melbourne 3010, VIC, Australia
| | - Saskia Freytag
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Sarah A Best
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Anthony T Papenfuss
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia; Peter MacCallum Cancer Centre, 305 Grattan St, Parkville, Melbourne 3010, VIC, Australia
| | - Sam W Z Olechnowicz
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Sarah E MacRaild
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia
| | - Stephen Wilcox
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia
| | - Peter F Hickey
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Daniela Amann-Zalcenstein
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia
| | - Rory Bowden
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade VIC, Melbourne 3052, VIC, Australia; Department of Medical Biology, University of Melbourne, Parkville, Melbourne 3010, VIC, Australia.
| |
Collapse
|
5
|
Howitt G, Feng Y, Tobar L, Vassiliadis D, Hickey P, Dawson M, Ranganathan S, Shanthikumar S, Neeland M, Maksimovic J, Oshlack A. Benchmarking single-cell hashtag oligo demultiplexing methods. NAR Genom Bioinform 2023; 5:lqad086. [PMID: 37829177 PMCID: PMC10566318 DOI: 10.1093/nargab/lqad086] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 08/25/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023] Open
Abstract
Sample multiplexing is often used to reduce cost and limit batch effects in single-cell RNA sequencing (scRNA-seq) experiments. A commonly used multiplexing technique involves tagging cells prior to pooling with a hashtag oligo (HTO) that can be sequenced along with the cells' RNA to determine their sample of origin. Several tools have been developed to demultiplex HTO sequencing data and assign cells to samples. In this study, we critically assess the performance of seven HTO demultiplexing tools: hashedDrops, HTODemux, GMM-Demux, demuxmix, deMULTIplex, BFF (bimodal flexible fitting) and HashSolo. The comparison uses data sets where each sample has also been demultiplexed using genetic variants from the RNA, enabling comparison of HTO demultiplexing techniques against complementary data from the genetic 'ground truth'. We find that all methods perform similarly where HTO labelling is of high quality, but methods that assume a bimodal count distribution perform poorly on lower quality data. We also suggest heuristic approaches for assessing the quality of HTO counts in an scRNA-seq experiment.
Collapse
Affiliation(s)
- George Howitt
- Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, 3010 Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010 Australia
| | - Yuzhou Feng
- Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, 3010 Australia
| | - Lucas Tobar
- Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, 3010 Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010 Australia
| | - Dane Vassiliadis
- Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, 3010 Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010 Australia
| | - Peter Hickey
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Mark A Dawson
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010 Australia
- Centre for Cancer Research, The University of Melbourne, Parkville, VIC, Australia
| | - Sarath Ranganathan
- Respiratory Diseases, Murdoch Children’s Research Institute, Parkville, VIC, Australia
- Respiratory and Sleep Medicine, Royal Children’s Hospital, Parkville, VIC, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
| | - Shivanthan Shanthikumar
- Respiratory Diseases, Murdoch Children’s Research Institute, Parkville, VIC, Australia
- Respiratory and Sleep Medicine, Royal Children’s Hospital, Parkville, VIC, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
| | - Melanie Neeland
- Respiratory Diseases, Murdoch Children’s Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
| | - Jovana Maksimovic
- Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, 3010 Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010 Australia
| | - Alicia Oshlack
- Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, 3010 Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010 Australia
- School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC, Australia
| |
Collapse
|
6
|
Garner LC, Amini A, FitzPatrick MEB, Lett MJ, Hess GF, Filipowicz Sinnreich M, Provine NM, Klenerman P. Single-cell analysis of human MAIT cell transcriptional, functional and clonal diversity. Nat Immunol 2023; 24:1565-1578. [PMID: 37580605 PMCID: PMC10457204 DOI: 10.1038/s41590-023-01575-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/26/2023] [Indexed: 08/16/2023]
Abstract
Mucosal-associated invariant T (MAIT) cells are innate-like T cells that recognize microbial metabolites through a semi-invariant T cell receptor (TCR). Major questions remain regarding the extent of human MAIT cell functional and clonal diversity. To address these, we analyzed the single-cell transcriptome and TCR repertoire of blood and liver MAIT cells and developed functional RNA-sequencing, a method to integrate function and TCR clonotype at single-cell resolution. MAIT cell clonal diversity was comparable to conventional memory T cells, with private TCR repertoires shared across matched tissues. Baseline functional diversity was low and largely related to tissue site. MAIT cells showed stimulus-specific transcriptional responses in vitro, with cells positioned along gradients of activation. Clonal identity influenced resting and activated transcriptional profiles but intriguingly was not associated with the capacity to produce IL-17. Overall, MAIT cells show phenotypic and functional diversity according to tissue localization, stimulation environment and clonotype.
Collapse
Affiliation(s)
- Lucy C Garner
- Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Ali Amini
- Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Michael E B FitzPatrick
- Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Martin J Lett
- Department of Biomedicine, Liver Immunology, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Gabriel F Hess
- Division of Visceral Surgery, Clarunis University Center for Gastrointestinal and Liver Diseases, Basel, Switzerland
| | - Magdalena Filipowicz Sinnreich
- Department of Biomedicine, Liver Immunology, University Hospital Basel and University of Basel, Basel, Switzerland
- Gastroenterology and Hepatology, University Department of Medicine, Cantonal Hospital Baselland, Liestal, Switzerland
| | - Nicholas M Provine
- Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Paul Klenerman
- Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK.
| |
Collapse
|
7
|
Huang LC, Stolze LK, Chen HC, Gelbard A, Shyr Y, Liu Q, Sheng Q. scDemultiplex: An iterative beta-binomial model-based method for accurate demultiplexing with hashtag oligos. Comput Struct Biotechnol J 2023; 21:4044-4055. [PMID: 37664174 PMCID: PMC10469060 DOI: 10.1016/j.csbj.2023.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/15/2023] [Accepted: 08/15/2023] [Indexed: 09/05/2023] Open
Abstract
Single-cell sequencing have been widely used to characterize cellular heterogeneity. Sample multiplexing where multiple samples are pooled together for single-cell experiments, attracts wide attention due to its benefits of increasing capacity, reducing costs, and minimizing batch effects. To analyze multiplexed data, the first crucial step is to demultiplex, the process of assigning cells to individual samples. Inaccurate demultiplexing will create false cell types and result in misleading characterization. We propose scDemultiplex, which models hashtag oligo (HTO) counts with beta-binomial distribution and uses an iterative strategy for further refinement. Compared with seven existing demultiplexing approaches, scDemultiplex achieved great performance in both high-quality and low-quality data. Additionally, scDemultiplex can be combined with other approaches to improve their performance.
Collapse
Affiliation(s)
- Li-Ching Huang
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lindsey K. Stolze
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hua-Chang Chen
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Alexander Gelbard
- Department of Otolaryngology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Yu Shyr
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Qi Liu
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Quanhu Sheng
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| |
Collapse
|