Method of the year 2013. Nat Methods. 2014;11(1):1. https://doi.org/10.1038/nmeth.2801.

Greenblatt MB, Ono N, Ayturk UM, Debnath S, Lalani S. The unmixing problem: a guide to applying single-cell RNA sequencing to bone. J Bone Miner Res. 2019;34(7):1207–19. https://doi.org/10.1002/jbmr.3802.

Article  Google Scholar 

Ono N, Taipaleenmaki H, Veis DJ. Single-cell RNA-sequencing leading to breakthroughs in musculoskeletal research. JBMR Plus. 2022;6(7):e10652. https://doi.org/10.1002/jbm4.10652.

Article  CAS  PubMed  PubMed Central  Google Scholar 

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.

Article  CAS  Google Scholar 

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.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Nakayama M, Okada H, Seki M, Suzuki Y, Chung UI, Ohba S, Hojo H. Single-cell RNA sequencing unravels heterogeneity of skeletal progenitors and cell-cell interactions underlying the bone repair process. Regen Ther. 2022;21:9–18. https://doi.org/10.1016/j.reth.2022.05.001.

Article  CAS  PubMed Central  Google Scholar 

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.

Article  CAS  PubMed  PubMed Central  Google Scholar 

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.

Kanazawa S, Okada H, Hojo H, Ohba S, Iwata J, Komura M, Hikita A, Hoshi K. Mesenchymal stromal cells in the bone marrow niche consist of multi-populations with distinct transcriptional and epigenetic properties. Sci Rep. 2021;11(1):15811. https://doi.org/10.1038/s41598-021-94186-5.

Article  CAS  PubMed  PubMed Central  Google Scholar 

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.

Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15(6):e8746. https://doi.org/10.15252/msb.20188746.

Article  PubMed  PubMed Central  Google Scholar 

Andrews TS, Kiselev VY, McCarthy D, Hemberg M. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nat Protoc. 2021;16(1):1–9. https://doi.org/10.1038/s41596-020-00409-w.

Article  CAS  PubMed  Google Scholar 

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.

Article  CAS  PubMed  Google Scholar 

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.

Article  CAS  PubMed  PubMed Central  Google Scholar 

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.

Article  CAS  PubMed Central  Google Scholar 

The SRA Toolkit Development Team: SRA toolkit. edn 3.0.3. Edited by; 2023.

Valieris R, Fukushima K, Homer N. parallel-fastq-dump. edn 0.6.7. Edited by; 2021.

Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, Daniels GA, Khrebtukova I, Loring JF, Laurent LC, Schroth GP, Sandberg R. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30(8):777–82. https://doi.org/10.1038/nbt.2282.

Article  CAS  PubMed  Google Scholar 

Hayashi T, Ozaki H, Sasagawa Y, Umeda M, Danno H, Nikaido I. Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs. Nat Commun. 2018;9(1):619. https://doi.org/10.1038/s41467-018-02866-0.

Article  CAS  PubMed  PubMed Central  Google Scholar 

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.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kaminow B, Yunusov D, Dobin A. STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data. bioRxiv. 2021.05.05.442755. https://doi.org/10.1101/2021.05.05.442755.

R Core Team. _R: a language and environment for statistical computing_. R Foundation for Statistical Computing, Vienna, Austria; 2023.

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.

Article  Google Scholar 

Wickham H. ggplot2: elegant graphics for data analysis: Springer-Verlag New York; 2009.

Van Rossum G, Drake FL. Python 3 reference manual: CreateSpace; 2009.

Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007;9:90–5. https://doi.org/10.1109/MCSE.2007.55.

Article  Google Scholar 

Waskom M. seaborn: statistical data visualization.  J Open Source Softw. 2021;6:3021. https://doi.org/10.21105/joss.03021.

Article  Google Scholar 

Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, Hoffman P, Stoeckius M, Papalexi E, Mimitou EP, Jain J, Srivastava A, Stuart T, Fleming LM, Yeung B, Rogers AJ, McElrath JM, Blish CA, Gottardo R, Smibert P, Satija R. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573–87.e29. https://doi.org/10.1016/j.cell.2021.04.048.

Article  CAS  PubMed  PubMed Central  Google Scholar 

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.

Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 2019;20(1):296. https://doi.org/10.1186/s13059-019-1874-1.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19(1):15. https://doi.org/10.1186/s13059-017-1382-0.

Article  PubMed  PubMed Central  Google Scholar 

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.

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.

Article  CAS  PubMed  Google Scholar 

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.

Article  CAS  PubMed  Google Scholar 

van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008:2579–2605.

Leland McInnes JH, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv. 2020. https://doi.org/10.48550/arXiv.1802.03426.

Gorzalczany MB, Rudzinski F. Generalized self-organizing maps for automatic determination of the number of clusters and their multiprototypes in cluster analysis. IEEE Trans Neural Netw Learn Syst. 2018;29(7):2833–45. https://doi.org/10.1109/TNNLS.2017.2704779.

Article  PubMed  Google Scholar 

Aibar S, Aerts S. AUCell. 2016.

Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, Rambow F, Marine JC, Geurts P, Aerts J, van den Oord J, Atak ZK, Wouters J, Aerts S. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083–6. https://doi.org/10.1038/nmeth.4463.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Van de Sande B, Flerin C, Davie K, De Waegeneer M, Hulselmans G, Aibar S, Seurinck R, Saelens W, Cannoodt R, Rouchon Q, Verbeiren T, De Maeyer D, Reumers J, Saeys Y, Aerts S. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc. 2020;15(7):2247–76. https://doi.org/10.1038/s41596-020-0336-2.

Article  CAS  PubMed  Google Scholar 

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.

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.