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Open Access 29-11-2023

Practical Compass of Single-Cell RNA-Seq Analysis

Authors: Hiroyuki Okada, Ung-il Chung, Hironori Hojo

Published in: Current Osteoporosis Reports

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Abstract

Purpose of Review

This review paper provides step-by-step instructions on the fundamental process, from handling fastq datasets to illustrating plots and drawing trajectories.

Recent Findings

The number of studies using single-cell RNA-seq (scRNA-seq) is increasing. scRNA-seq revealed the heterogeneity or diversity of the cellular populations. scRNA-seq also provides insight into the interactions between different cell types. User-friendly scRNA-seq packages for ligand-receptor interactions and trajectory analyses are available. In skeletal biology, osteoclast differentiation, fracture healing, ectopic ossification, human bone development, and the bone marrow niche have been examined using scRNA-seq. scRNA-seq data analysis tools are still being developed, even at the fundamental step of dataset integration. However, updating the latest information is difficult for many researchers. Investigators and reviewers must share their knowledge of in silico scRNA-seq for better biological interpretation.

Summary

This review article aims to provide a useful guide for complex analytical processes in single-cell RNA-seq data analysis.
Literature
4.
go back to reference Tsukasaki M, Huynh NC, Okamoto K, Muro R, Terashima A, Kurikawa Y, Komatsu N, Pluemsakunthai W, Nitta T, Abe T, Kiyonari H, Okamura T, Sakai M, Matsukawa T, Matsumoto M, Kobayashi Y, Penninger JM, Takayanagi H. Stepwise cell fate decision pathways during osteoclastogenesis at single-cell resolution. Nat Metab. 2020;2(12):1382–90. https://doi.org/10.1038/s42255-020-00318-y.CrossRefPubMed Tsukasaki M, Huynh NC, Okamoto K, Muro R, Terashima A, Kurikawa Y, Komatsu N, Pluemsakunthai W, Nitta T, Abe T, Kiyonari H, Okamura T, Sakai M, Matsukawa T, Matsumoto M, Kobayashi Y, Penninger JM, Takayanagi H. Stepwise cell fate decision pathways during osteoclastogenesis at single-cell resolution. Nat Metab. 2020;2(12):1382–90. https://​doi.​org/​10.​1038/​s42255-020-00318-y.CrossRefPubMed
5.•
go back to reference Omata Y, Okada H, Uebe S, Izawa N, Ekici AB, Sarter K, Saito T, Schett G, Tanaka S, Zaiss MM. Interspecies single-Cell RNA-Seq analysis reveals the novel trajectory of osteoclast differentiation and therapeutic targets. JBMR Plus. 2022;6(7):e10631. https://doi.org/10.1002/jbm4.10631. Interspecies difference in osteoclast differentiation path was depicted using single cell RNA-seq.CrossRefPubMedPubMedCentral Omata Y, Okada H, Uebe S, Izawa N, Ekici AB, Sarter K, Saito T, Schett G, Tanaka S, Zaiss MM. Interspecies single-Cell RNA-Seq analysis reveals the novel trajectory of osteoclast differentiation and therapeutic targets. JBMR Plus. 2022;6(7):e10631. https://​doi.​org/​10.​1002/​jbm4.​10631. Interspecies difference in osteoclast differentiation path was depicted using single cell RNA-seq.CrossRefPubMedPubMedCentral
7.
go back to reference Tachibana N, Chijimatsu R, Okada H, Oichi T, Taniguchi Y, Maenohara Y, Miyahara J, Ishikura H, Iwanaga Y, Arino Y, Nagata K, Nakamoto H, Kato S, Doi T, Matsubayashi Y, Oshima Y, Terashima A, Omata Y, Yano F, Maeda S, Ikegawa S, Seki M, Suzuki Y, Tanaka S, Saito T. RSPO2 defines a distinct undifferentiated progenitor in the tendon/ligament and suppresses ectopic ossification. Sci Adv. 2022;8(33):eabn2138. https://doi.org/10.1126/sciadv.abn2138.CrossRefPubMedPubMedCentral Tachibana N, Chijimatsu R, Okada H, Oichi T, Taniguchi Y, Maenohara Y, Miyahara J, Ishikura H, Iwanaga Y, Arino Y, Nagata K, Nakamoto H, Kato S, Doi T, Matsubayashi Y, Oshima Y, Terashima A, Omata Y, Yano F, Maeda S, Ikegawa S, Seki M, Suzuki Y, Tanaka S, Saito T. RSPO2 defines a distinct undifferentiated progenitor in the tendon/ligament and suppresses ectopic ossification. Sci Adv. 2022;8(33):eabn2138. https://​doi.​org/​10.​1126/​sciadv.​abn2138.CrossRefPubMedPubMedCentral
8.•
go back to reference Tani S, Okada H, Onodera S, Chijimatsu R, Seki M, Suzuki Y, Xin X, Rowe DW, Saito T, Tanaka S, Chung UI, Ohba S, Hojo H. Stem cell-based modeling and single-cell multiomics reveal gene-regulatory mechanisms underlying human skeletal development. Cell Rep. 2023;42(4):112276. https://doi.org/10.1016/j.celrep.2023.112276. Transcriptomic and epigenetic human bone development was illustrated by a novel multi-omics approach. Tani S, Okada H, Onodera S, Chijimatsu R, Seki M, Suzuki Y, Xin X, Rowe DW, Saito T, Tanaka S, Chung UI, Ohba S, Hojo H. Stem cell-based modeling and single-cell multiomics reveal gene-regulatory mechanisms underlying human skeletal development. Cell Rep. 2023;42(4):112276. https://​doi.​org/​10.​1016/​j.​celrep.​2023.​112276. Transcriptomic and epigenetic human bone development was illustrated by a novel multi-omics approach.
10.•
go back to reference Okada H, Terui Y, Omata Y, Terashima A, Seki M, Tani S, Kanazawa S, Hosonuma M, Miyahara J, Makabe K, Onodera S, Yano F, Kajiya H, Gori F, Saito T, Suzuki Y, Okabe K, Baron R, Chung UI, Tanaka S, Hojo H. Inclusive living subcellular sequencing rendering physical physiological and human pathological features in osteoimmune diversity. bioRxiv. 2022.09.05.506360. https://doi.org/10.1101/2022.09.05.506360. The technology for live subcellular sequencing was achieved. Okada H, Terui Y, Omata Y, Terashima A, Seki M, Tani S, Kanazawa S, Hosonuma M, Miyahara J, Makabe K, Onodera S, Yano F, Kajiya H, Gori F, Saito T, Suzuki Y, Okabe K, Baron R, Chung UI, Tanaka S, Hojo H. Inclusive living subcellular sequencing rendering physical physiological and human pathological features in osteoimmune diversity. bioRxiv. 2022.09.05.506360. https://​doi.​org/​10.​1101/​2022.​09.​05.​506360. The technology for live subcellular sequencing was achieved.
13.
go back to reference Baccin C, Al-Sabah J, Velten L, Helbling PM, Grunschlager F, Hernandez-Malmierca P, Nombela-Arrieta C, Steinmetz LM, Trumpp A, Haas S. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. Nat Cell Biol. 2020;22(1):38–48. https://doi.org/10.1038/s41556-019-0439-6.CrossRefPubMed Baccin C, Al-Sabah J, Velten L, Helbling PM, Grunschlager F, Hernandez-Malmierca P, Nombela-Arrieta C, Steinmetz LM, Trumpp A, Haas S. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. Nat Cell Biol. 2020;22(1):38–48. https://​doi.​org/​10.​1038/​s41556-019-0439-6.CrossRefPubMed
14.
go back to reference Wang JS, Kamath T, Mazur CM, Mirzamohammadi F, Rotter D, Hojo H, Castro CD, Tokavanich N, Patel R, Govea N, Enishi T, Wu Y, da Silva Martins J, Bruce M, Brooks DJ, Bouxsein ML, Tokarz D, Lin CP, Abdul A, Macosko EZ, Fiscaletti M, Munns CF, Ryder P, Kost-Alimova M, Byrne P, Cimini B, Fujiwara M, Kronenberg HM, Wein MN. Control of osteocyte dendrite formation by Sp7 and its target gene osteocrin. Nat Commun. 2021;12(1):6271. https://doi.org/10.1038/s41467-021-26571-7.ADSCrossRefPubMedPubMedCentral Wang JS, Kamath T, Mazur CM, Mirzamohammadi F, Rotter D, Hojo H, Castro CD, Tokavanich N, Patel R, Govea N, Enishi T, Wu Y, da Silva Martins J, Bruce M, Brooks DJ, Bouxsein ML, Tokarz D, Lin CP, Abdul A, Macosko EZ, Fiscaletti M, Munns CF, Ryder P, Kost-Alimova M, Byrne P, Cimini B, Fujiwara M, Kronenberg HM, Wein MN. Control of osteocyte dendrite formation by Sp7 and its target gene osteocrin. Nat Commun. 2021;12(1):6271. https://​doi.​org/​10.​1038/​s41467-021-26571-7.ADSCrossRefPubMedPubMedCentral
15.
go back to reference Zhong L, Yao L, Tower RJ, Wei Y, Miao Z, Park J, Shrestha R, Wang L, Yu W, Holdreith N, Huang X, Zhang Y, Tong W, Gong Y, Ahn J, Susztak K, Dyment N, Li M, Long F, Chen C, Seale P, Qin L. Single cell transcriptomics identifies a unique adipose lineage cell population that regulates bone marrow environment. Elife. 2020;9:e54695. https://doi.org/10.7554/eLife.54695.CrossRefPubMedPubMedCentral Zhong L, Yao L, Tower RJ, Wei Y, Miao Z, Park J, Shrestha R, Wang L, Yu W, Holdreith N, Huang X, Zhang Y, Tong W, Gong Y, Ahn J, Susztak K, Dyment N, Li M, Long F, Chen C, Seale P, Qin L. Single cell transcriptomics identifies a unique adipose lineage cell population that regulates bone marrow environment. Elife. 2020;9:e54695. https://​doi.​org/​10.​7554/​eLife.​54695.CrossRefPubMedPubMedCentral
16.
go back to reference The SRA Toolkit Development Team: SRA toolkit. edn 3.0.3. Edited by; 2023. The SRA Toolkit Development Team: SRA toolkit. edn 3.0.3. Edited by; 2023.
17.
go back to reference Valieris R, Fukushima K, Homer N. parallel-fastq-dump. edn 0.6.7. Edited by; 2021. Valieris R, Fukushima K, Homer N. parallel-fastq-dump. edn 0.6.7. Edited by; 2021.
20.
go back to reference Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, Gregory MT, Shuga J, Montesclaros L, Underwood JG, Masquelier DA, Nishimura SY, Schnall-Levin M, Wyatt PW, Hindson CM, Bharadwaj R, Wong A, Ness KD, Beppu LW, Deeg HJ, McFarland C, Loeb KR, Valente WJ, Ericson NG, Stevens EA, Radich JP, Mikkelsen TS, Hindson BJ, Bielas JH. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049. https://doi.org/10.1038/ncomms14049.ADSCrossRefPubMedPubMedCentral Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, Gregory MT, Shuga J, Montesclaros L, Underwood JG, Masquelier DA, Nishimura SY, Schnall-Levin M, Wyatt PW, Hindson CM, Bharadwaj R, Wong A, Ness KD, Beppu LW, Deeg HJ, McFarland C, Loeb KR, Valente WJ, Ericson NG, Stevens EA, Radich JP, Mikkelsen TS, Hindson BJ, Bielas JH. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049. https://​doi.​org/​10.​1038/​ncomms14049.ADSCrossRefPubMedPubMedCentral
22.
go back to reference R Core Team. _R: a language and environment for statistical computing_. R Foundation for Statistical Computing, Vienna, Austria; 2023. R Core Team. _R: a language and environment for statistical computing_. R Foundation for Statistical Computing, Vienna, Austria; 2023.
23.
go back to reference Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, Grolemund G, Hayes A, Henry L, Hester J, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4(43):1686 10.21105/joss.01686.ADSCrossRef Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, Grolemund G, Hayes A, Henry L, Hester J, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4(43):1686 10.21105/joss.01686.ADSCrossRef
24.
go back to reference Wickham H. ggplot2: elegant graphics for data analysis: Springer-Verlag New York; 2009. Wickham H. ggplot2: elegant graphics for data analysis: Springer-Verlag New York; 2009.
25.
go back to reference Van Rossum G, Drake FL. Python 3 reference manual: CreateSpace; 2009. Van Rossum G, Drake FL. Python 3 reference manual: CreateSpace; 2009.
29.••
go back to reference Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-Granda C, Satija R. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol. 2023. https://doi.org/10.1038/s41587-023-01767-y. The fundamental package Seurat in scRNA-seq analysis was updated to version 5. Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-Granda C, Satija R. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol. 2023. https://​doi.​org/​10.​1038/​s41587-023-01767-y. The fundamental package Seurat in scRNA-seq analysis was updated to version 5.
32.
go back to reference Virshup I, Bredikhin D, Heumos L, Palla G, Sturm G, Gayoso A, Kats I, Koutrouli M; Scverse Community; Berger B, Pe'er D, Regev A, Teichmann SA, Finotello F, Wolf FA, Yosef N, Stegle O, Theis FJ. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nat Biotechnol. 2023;41(5):604–6. https://doi.org/10.1038/s41587-023-01733-8. Virshup I, Bredikhin D, Heumos L, Palla G, Sturm G, Gayoso A, Kats I, Koutrouli M; Scverse Community; Berger B, Pe'er D, Regev A, Teichmann SA, Finotello F, Wolf FA, Yosef N, Stegle O, Theis FJ. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nat Biotechnol. 2023;41(5):604–6. https://​doi.​org/​10.​1038/​s41587-023-01733-8.
33.•
go back to reference Luecken MD, Buttner M, Chaichoompu K, Danese A, Interlandi M, Mueller MF, Strobl DC, Zappia L, Dugas M, Colome-Tatche M, Theis FJ. Benchmarking atlas-level data integration in single-cell genomics. Nat Methods. 2022;19(1):41–50. https://doi.org/10.1038/s41592-021-01336-8. This benckmark study provided a clue to choose the appropriate integration method of scRNA-seq.CrossRefPubMed Luecken MD, Buttner M, Chaichoompu K, Danese A, Interlandi M, Mueller MF, Strobl DC, Zappia L, Dugas M, Colome-Tatche M, Theis FJ. Benchmarking atlas-level data integration in single-cell genomics. Nat Methods. 2022;19(1):41–50. https://​doi.​org/​10.​1038/​s41592-021-01336-8. This benckmark study provided a clue to choose the appropriate integration method of scRNA-seq.CrossRefPubMed
34.•
go back to reference Gayoso A, Lopez R, Xing G, Boyeau P, Valiollah Pour Amiri V, Hong J, Wu K, Jayasuriya M, Mehlman E, Langevin M, Liu Y, Samaran J, Misrachi G, Nazaret A, Clivio O, Xu C, Ashuach T, Gabitto M, Lotfollahi M, Svensson V, da Veiga Beltrame E, Kleshchevnikov V, Talavera-López C, Pachter L, Theis FJ, Streets A, Jordan MI, Regier J, Yosef N. A Python library for probabilistic analysis of single-cell omics data. Nat Biotechnol. 2022;40(2):163–6. https://doi.org/10.1038/s41587-021-01206-w. Quality of automatic cell annotation was improved with machine learning technique.CrossRefPubMed Gayoso A, Lopez R, Xing G, Boyeau P, Valiollah Pour Amiri V, Hong J, Wu K, Jayasuriya M, Mehlman E, Langevin M, Liu Y, Samaran J, Misrachi G, Nazaret A, Clivio O, Xu C, Ashuach T, Gabitto M, Lotfollahi M, Svensson V, da Veiga Beltrame E, Kleshchevnikov V, Talavera-López C, Pachter L, Theis FJ, Streets A, Jordan MI, Regier J, Yosef N. A Python library for probabilistic analysis of single-cell omics data. Nat Biotechnol. 2022;40(2):163–6. https://​doi.​org/​10.​1038/​s41587-021-01206-w. Quality of automatic cell annotation was improved with machine learning technique.CrossRefPubMed
35.
go back to reference van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008:2579–2605. van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008:2579–2605.
38.
40.
41.•
go back to reference Badia IMP, Velez Santiago J, Braunger J, Geiss C, Dimitrov D, Muller-Dott S, Taus P, Dugourd A, Holland CH, Ramirez Flores RO, et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinform Adv. 2022;2:vbac016. decoupleR proveids ensemble methods of various annotation tools. Badia IMP, Velez Santiago J, Braunger J, Geiss C, Dimitrov D, Muller-Dott S, Taus P, Dugourd A, Holland CH, Ramirez Flores RO, et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinform Adv. 2022;2:vbac016. decoupleR proveids ensemble methods of various annotation tools.
42.
go back to reference Zhang AW, O’Flanagan C, Chavez EA, Lim JLP, Ceglia N, McPherson A, Wiens M, Walters P, Chan T, Hewitson B, Lai D, Mottok A, Sarkozy C, Chong L, Aoki T, Wang X, Weng AP, McAlpine JN, Aparicio S, Steidl C, Campbell KR, Shah SP. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat Methods. 2019;16(10):1007–15. https://doi.org/10.1038/s41592-019-0529-1.CrossRefPubMedPubMedCentral Zhang AW, O’Flanagan C, Chavez EA, Lim JLP, Ceglia N, McPherson A, Wiens M, Walters P, Chan T, Hewitson B, Lai D, Mottok A, Sarkozy C, Chong L, Aoki T, Wang X, Weng AP, McAlpine JN, Aparicio S, Steidl C, Campbell KR, Shah SP. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat Methods. 2019;16(10):1007–15. https://​doi.​org/​10.​1038/​s41592-019-0529-1.CrossRefPubMedPubMedCentral
49.
go back to reference Qiu X, Zhang Y, Martin-Rufino JD, Weng C, Hosseinzadeh S, Yang D, Pogson AN, Hein MY, Hoi Joseph Min K, Wang L, Grody EI, Shurtleff MJ, Yuan R, Xu S, Ma Y, Replogle JM, Lander ES, Darmanis S, Bahar I, Sankaran VG, Xing J, Weissman JS. Mapping transcriptomic vector fields of single cells. Cell. 2022;185(4):690–711.e45. https://doi.org/10.1016/j.cell.2021.12.045. Qiu X, Zhang Y, Martin-Rufino JD, Weng C, Hosseinzadeh S, Yang D, Pogson AN, Hein MY, Hoi Joseph Min K, Wang L, Grody EI, Shurtleff MJ, Yuan R, Xu S, Ma Y, Replogle JM, Lander ES, Darmanis S, Bahar I, Sankaran VG, Xing J, Weissman JS. Mapping transcriptomic vector fields of single cells. Cell. 2022;185(4):690–711.e45. https://​doi.​org/​10.​1016/​j.​cell.​2021.​12.​045.
Metadata
Title
Practical Compass of Single-Cell RNA-Seq Analysis
Authors
Hiroyuki Okada
Ung-il Chung
Hironori Hojo
Publication date
29-11-2023
Publisher
Springer US
Published in
Current Osteoporosis Reports
Print ISSN: 1544-1873
Electronic ISSN: 1544-2241
DOI
https://doi.org/10.1007/s11914-023-00840-4