Journal Home > Volume 17 , Issue 1
Nano Research 2024, 17(1): 132-150 https://doi.org/10.1007/s12274-023-5924-3
Review Article | Issue | Published: 01 August 2023

Recent progress in co-detection of single-cell transcripts and proteins

Show Author's Information Shanqing Huang1,§,  Qian Fan2,4,§,  Yidi Wang1,  Zhi Huang2,  Weixiong Shi1,  Yanli Gong2,  Ting Yang4,  Jie Wang3( ),  Lingling Wu2( ),  Chaoyong Yang1,2( )
Discipline of Intelligent Instrument and Equipment, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
Department of General Surgery, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, China

§ Shanqing Huang and Qian Fan contributed equally to this work.

Keywords:
optical imaging, mass spectrometry imaging, single-cell RNA sequencing (scRNA-seq), single cell, co-detection
Topics:
Cells
Cite this article:
Huang S, Fan Q, Wang Y, et al. Recent progress in co-detection of single-cell transcripts and proteins. Nano Research, 2024, 17(1): 132-150. https://doi.org/10.1007/s12274-023-5924-3
Download citation
EndNote(RIS)
BibTeX

371

Views

40

Downloads

Citations

1

Crossref

0

WoS

0

Scopus

0

CSCD

Abstract Full text About this article

Cellular heterogeneity is a universal property of living systems, and the interrogation of single cells facilitates in-depth understanding of distinct cellular states and functions in various biological processes. Co-analysis of transcripts and proteins from the same single cells opens the way to decipher complex RNA regulatory frameworks and phenotypes, facilitating the understanding of cellular fate and function regulations, discovery of novel cell types, and construction of a high-resolution cell atlas. Herein, we review the state-of-art advances in the development of methodologies for co-analysis of single-cell transcripts and proteins. First, imaging-based methods are summarized with particular emphasis on optical and mass spectrometry imaging. Next, sequencing-based approaches for high-throughput and sensitive co-analysis of single-cell transcripts and proteins are described, including droplet-, microwell-, and split-pool-based platforms. Subsequently, combined methods with more flexibility and universality are discussed. These methods commonly employ different strategies or reactions to convert transcripts and proteins of single cells into distinct signals simultaneously, which can be detected by different instruments or platforms. Lastly, some perspectives on the future challenges and development trends in this field are presented.


menu
Abstract
Full text
Outline
About this article

Recent progress in co-detection of single-cell transcripts and proteins

Show Author's information Shanqing Huang1,§, Qian Fan2,4,§, Yidi Wang1, Zhi Huang2, Weixiong Shi1, Yanli Gong2, Ting Yang4, Jie Wang3( ), Lingling Wu2( ), Chaoyong Yang1,2( )
Discipline of Intelligent Instrument and Equipment, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
Department of General Surgery, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, China

§ Shanqing Huang and Qian Fan contributed equally to this work.

Abstract

Cellular heterogeneity is a universal property of living systems, and the interrogation of single cells facilitates in-depth understanding of distinct cellular states and functions in various biological processes. Co-analysis of transcripts and proteins from the same single cells opens the way to decipher complex RNA regulatory frameworks and phenotypes, facilitating the understanding of cellular fate and function regulations, discovery of novel cell types, and construction of a high-resolution cell atlas. Herein, we review the state-of-art advances in the development of methodologies for co-analysis of single-cell transcripts and proteins. First, imaging-based methods are summarized with particular emphasis on optical and mass spectrometry imaging. Next, sequencing-based approaches for high-throughput and sensitive co-analysis of single-cell transcripts and proteins are described, including droplet-, microwell-, and split-pool-based platforms. Subsequently, combined methods with more flexibility and universality are discussed. These methods commonly employ different strategies or reactions to convert transcripts and proteins of single cells into distinct signals simultaneously, which can be detected by different instruments or platforms. Lastly, some perspectives on the future challenges and development trends in this field are presented.

Keywords: optical imaging, mass spectrometry imaging, single-cell RNA sequencing (scRNA-seq), single cell, co-detection

References(133)

[1]

Yuan, G. C.; Cai, L.; Elowitz, M.; Enver, T.; Fan, G. P.; Guo, G. J.; Irizarry, R.; Kharchenko, P.; Kim, J.; Orkin, S. et al. Challenges and emerging directions in single-cell analysis. Genome Biol. 2017, 18, 84.
DOI Google Scholar
[2]

Saadatpour, A.; Lai, S. J.; Guo, G. J.; Yuan, G. C. Single-cell analysis in cancer genomics. Trends Genet. 2015, 31, 576–586.
DOI Google Scholar
[3]

Toriello, N. M.; Douglas, E. S.; Thaitrong, N.; Hsiao, S. C.; Francis, M. B.; Bertozzi, C. R.; Mathies, R. A. Integrated microfluidic bioprocessor for single-cell gene expression analysis. Proc. Natl. Acad. Sci. USA 2008, 105, 20173–20178.
DOI Google Scholar
[4]

Piras, V.; Tomita, M.; Selvarajoo, K. Is central dogma a global property of cellular information flow? Front. Physiol. 2012, 3, 439.
DOI Google Scholar
[5]

Cech, T. R. The RNA worlds in context. Cold Spring Harb Perspect. Biol. 2012, 4, a006742.
DOI Google Scholar
[6]

Wu, M. Y.; Singh, A. K. Single-cell protein analysis. Curr. Opin. Biotechnol. 2012, 23, 83–88.
DOI Google Scholar
[7]

Wu, A. R.; Wang, J. B.; Streets, A. M.; Huang, Y. Y. Single-cell transcriptional analysis. Annu. Rev. Anal. Chem. 2017, 10, 439–462.
DOI Google Scholar
[8]

Grün, D.; Lyubimova, A.; Kester, L.; Wiebrands, K.; Basak, O.; Sasaki, N.; Clevers, H.; Van Oudenaarden, A. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 2015, 525, 251–255.
DOI Google Scholar
[9]

Vistain, L. F.; Tay, S. Single-cell proteomics. Trends Biochem. Sci. 2021, 46, 661–672.
DOI Google Scholar
[10]

Battle, A.; Khan, Z.; Wang, S. H.; Mitrano, A.; Ford, M. J.; Pritchard, J. K.; Gilad, Y. Impact of regulatory variation from RNA to protein. Science 2014, 347, 664–667.
DOI Google Scholar
[11]

Li, J. J.; Biggin, M. D. Statistics requantitates the central dogma. Science 2015, 347, 1066–1067.
DOI Google Scholar
[12]

Kristensen, A. R.; Gsponer, J.; Foster, L. J. Protein synthesis rate is the predominant regulator of protein expression during differentiation. Mol. Syst. Biol. 2013, 9, 689.
DOI Google Scholar
[13]

Schwanhäusser, B.; Busse, D.; Li, N.; Dittmar, G.; Schuchhardt, J.; Wolf, J.; Chen, W.; Selbach, M. Global quantification of mammalian gene expression control. Nature 2011, 473, 337–342.
DOI Google Scholar
[14]

Mondal, M.; Liao, R. J.; Guo, J. Highly multiplexed single-cell protein analysis. Chem.—Eur. J. 2018, 24, 7083–7091.
DOI Google Scholar
[15]

Li, R.; Zou, Z. Y.; Wang, W. T.; Zou, P. Metabolic incorporation of electron-rich ribonucleosides enhances APEX-seq for profiling spatially restricted nascent transcriptome. Cell Chem. Biol. 2022, 29, 1218–1231.e8.
DOI Google Scholar
[16]

Schmid, A.; Kortmann, H.; Dittrich, P. S.; Blank, L. M. Chemical and biological single cell analysis. Curr. Opin. Biotechnol. 2010, 21, 12–20.
DOI Google Scholar
[17]

Xie, H. Y.; Ding, X. T. The intriguing landscape of single-cell protein analysis. Adv. Sci. 2022, 9, 2105932.
DOI Google Scholar
[18]

Tang, F. C.; Barbacioru, C.; Wang, Y. Z.; Nordman, E.; Lee, C.; Xu, N. L.; Wang, X. H.; Bodeau, J.; Tuch, B. B.; Siddiqui, A. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 2009, 6, 377–382.
DOI Google Scholar
[19]
Method of the year 2013. Nat. Methods 2014, 11, 1.
[20]

Ramsköld, D.; Luo, S. J.; Wang, Y. C.; Li, R.; Deng, Q. L.; Faridani, O. R.; Daniels, G. A.; Khrebtukova, I.; Loring, J. F.; Laurent, L. C. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 2012, 30, 777–782.
DOI Google Scholar
[21]

Picelli, S.; Faridani, O. R.; Björklund, Å. K.; Winberg, G.; Sagasser, S.; Sandberg, R. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 2014, 9, 171–181.
DOI Google Scholar
[22]

Rūmnieks, J.; Tārs, K. Protein–RNA interactions in the single-stranded RNA bacteriophages. Subcell. Biochem. 2018, 88, 281–303.
DOI Google Scholar
[23]

Lionnet, T.; Czaplinski, K.; Darzacq, X.; Shav-Tal, Y.; Wells, A. L.; Chao, J. A.; Park, H. Y.; De Turris, V.; Lopez-Jones, M.; Singer, R. H. A transgenic mouse for in vivo detection of endogenous labeled mRNA. Nat. Methods 2011, 8, 165–170.
DOI Google Scholar
[24]

Han, Y.; Wang, S. F.; Zhang, Z. P.; Ma, X. H.; Li, W.; Zhang, X. W.; Deng, J. Y.; Wei, H. P.; Li, Z. Y.; Zhang, X. E. et al. In vivo imaging of protein-protein and RNA–protein interactions using novel far-red fluorescence complementation systems. Nucleic Acids Res. 2014, 42, e103.
DOI Google Scholar
[25]

Katz, Z. B.; English, B. P.; Lionnet, T.; Yoon, Y. J.; Monnier, N.; Ovryn, B.; Bathe, M.; Singer, R. H. Mapping translation “hot-spots” in live cells by tracking single molecules of mRNA and ribosomes. eLife 2016, 5, e10415.
DOI Google Scholar
[26]

Chouaib, R.; Safieddine, A.; Pichon, X.; Imbert, A.; Kwon, O. S.; Samacoits, A.; Traboulsi, A. M.; Robert, M. C.; Tsanov, N.; Coleno, E. et al. A dual protein-mRNA localization screen reveals compartmentalized translation and widespread co-translational RNA targeting. Dev. Cell 2020, 54, 773–791.e5.
DOI Google Scholar
[27]

Taniguchi, Y.; Choi, P. J.; Li, G. W.; Chen, H. Y.; Babu, M.; Hearn, J.; Emili, A.; Xie, X. S. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 2010, 329, 533–538.
DOI Google Scholar
[28]

Akita, H.; Umetsu, Y.; Kurihara, D.; Harashima, H. Dual imaging of mRNA and protein production: An investigation of the mechanism of heterogeneity in cationic lipid-mediated transgene expression. Int. J. Pharm. 2011, 415, 218–220.
DOI Google Scholar
[29]

Xu, H.; Sepúlveda, L. A.; Figard, L.; Sokac, A. M.; Golding, I. Combining protein and mRNA quantification to decipher transcriptional regulation. Nat. Methods 2015, 12, 739–742.
DOI Google Scholar
[30]

Kochan, J.; Wawro, M.; Kasza, A. Simultaneous detection of mRNA and protein in single cells using immunofluorescence-combined single-molecule RNA FISH. Biotechniques 2015, 59, 209–221.
DOI Google Scholar
[31]

Eliscovich, C.; Shenoy, S. M.; Singer, R. H. Imaging mRNA and protein interactions within neurons. Proc. Natl. Acad. Sci. USA 2017, 114, E1875–E1884.
DOI Google Scholar
[32]

Morrison, J. A.; McKinney, M. C.; Kulesa, P. M. Resolving in vivo gene expression during collective cell migration using an integrated RNAscope, immunohistochemistry and tissue clearing method. Mech. Dev. 2017, 148, 100–106.
DOI Google Scholar
[33]

Junger, H.; Dobi, D.; Chen, A.; Lee, L.; Vasquez, J. J.; Tang, Q. Z.; Laszik, Z. G. Novel in situ hybridization and multiplex immunofluorescence technology combined with whole-slide digital image analysis in kidney transplantation. J. Histochem. Cytochem. 2020, 68, 445–459.
DOI Google Scholar
[34]

Vu, T.; Vallmitjana, A.; Gu, J.; La, K.; Xu, Q.; Flores, J.; Zimak, J.; Shiu, J.; Hosohama, L.; Wu, J. et al. Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis. Nat. Commun. 2022, 13, 169.
DOI Google Scholar
[35]

Ranjan, A. K.; Joglekar, M. V.; Atre, A. N.; Patole, M.; Bhonde, R. R.; Hardikar, A. Simultaneous imaging of microRNA or mRNA territories with protein territory in mammalian cells at single cell resolution. RNA Biol. 2012, 9, 949–953.
DOI Google Scholar
[36]

Basiji, D. A.; Ortyn, W. E.; Liang, L. C.; Venkatachalam, V.; Morrissey, P. Cellular image analysis and imaging by flow cytometry. Clin. Lab. Med. 2007, 27, 653–670.
DOI Google Scholar
[37]

Pekle, E.; Smith, A.; Rosignoli, G.; Sellick, C.; Smales, C. M.; Pearce, C. Application of imaging flow cytometry for the characterization of intracellular attributes in Chinese hamster ovary cell lines at the single-cell level. Biotechnol. J. 2019, 14, 1800675.
DOI Google Scholar
[38]

Giesen, C.; Wang, H. A. O.; Schapiro, D.; Zivanovic, N.; Jacobs, A.; Hattendorf, B.; Schüffler, P. J.; Grolimund, D.; Buhmann, J. M.; Brandt, S. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 2014, 11, 417–422.
DOI Google Scholar
[39]

Sämfors, S.; Fletcher, J. S. Lipid diversity in cells and tissue using imaging SIMS. Annu. Rev. Anal. Chem. 2020, 13, 249–271.
DOI Google Scholar
[40]

Wang, F.; Flanagan, J.; Su, N.; Wang, L. C.; Bui, S.; Nielson, A.; Wu, X. Y.; Vo, H. T.; Ma, X. J.; Luo, Y. L. RNAscope: A novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 2012, 14, 22–29.
DOI Google Scholar
[41]

Schulz, D.; Zanotelli, V. R. T.; Fischer, J. R.; Schapiro, D.; Engler, S.; Lun, X. K.; Jackson, H. W.; Bodenmiller, B. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 2018, 6, 25–36.e5.
DOI Google Scholar
[42]

Keren, L.; Bosse, M.; Thompson, S.; Risom, T.; Vijayaragavan, K.; McCaffrey, E.; Marquez, D.; Angoshtari, R.; Greenwald, N. F.; Fienberg, H. et al. MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci. Adv. 2019, 5, eaax5851.
DOI Google Scholar
[43]

Jiang, S. Z.; Chan, C. N.; Rovira-Clavé, X.; Chen, H.; Bai, Y. H.; Zhu, B. K.; McCaffrey, E.; Greenwald, N. F.; Liu, C.; Barlow, G. L. et al. Combined protein and nucleic acid imaging reveals virus-dependent B cell and macrophage immunosuppression of tissue microenvironments. Immunity 2022, 55, 1118–1134.e8.
DOI Google Scholar
[44]

Le, M. U. T.; Shon, H. K.; Nguyen, H. P.; Lee, C. H.; Kim, K. S.; Na, H. K.; Lee, T. G. Simultaneous multiplexed imaging of biomolecules in transgenic mouse brain tissues using mass spectrometry imaging: A multi-omic approach. Anal. Chem. 2022, 94, 9297–9305.
DOI Google Scholar
[45]

Shendure, J.; Balasubramanian, S.; Church, G. M.; Gilbert, W.; Rogers, J.; Schloss, J. A.; Waterston, R. H. DNA sequencing at 40: Past, present and future. Nature 2017, 550, 345–353.
DOI Google Scholar
[46]

Wang, Z.; Gerstein, M.; Snyder, M. RNA-Seq: A revolutionary tool for transcriptomics. Nat. Rev. Genet. 2009, 10, 57–63.
DOI Google Scholar
[47]

Streets, A. M.; Zhang, X. N.; Cao, C.; Pang, Y. H.; Wu, X. L.; Xiong, L.; Yang, L.; Fu, Y. S.; Zhao, L.; Tang, F. C. et al. Microfluidic single-cell whole-transcriptome sequencing. Proc. Natl. Acad. Sci. USA 2014, 111, 7048–7053.
DOI Google Scholar
[48]

Xin, Y. R.; Kim, J.; Ni, M.; Wei, Y.; Okamoto, H.; Lee, J.; Adler, C.; Cavino, K.; Murphy, A. J.; Yancopoulos, G. D. et al. Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. Proc. Natl. Acad. Sci. USA 2016, 113, 3293–3298.
DOI Google Scholar
[49]

Xu, X.; Zhang, Q. Q.; Song, J.; Ruan, Q. Y.; Ruan, W. D.; Chen, Y. J.; Yang, J.; Zhang, X. B.; Song, Y. L.; Zhu, Z. et al. A highly sensitive, accurate, and automated single-cell RNA sequencing platform with digital microfluidics. Anal. Chem. 2020, 92, 8599–8606.
DOI Google Scholar
[50]

Macosko, E. Z.; Basu, A.; Satija, R.; Nemesh, J.; Shekhar, K.; Goldman, M.; Tirosh, I.; Bialas, A. R.; Kamitaki, N.; Martersteck, E. M. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 2015, 161, 1202–1214.
DOI Google Scholar
[51]

Klein, A. M.; Mazutis, L.; Akartuna, I.; Tallapragada, N.; Veres, A.; Li, V.; Peshkin, L.; Weitz, D. A.; Kirschner, M. W. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 2015, 161, 1187–1201.
DOI Google Scholar
[52]

Fan, H. C.; Fu, G. K.; Fodor, S. P. A. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 2015, 347, 1258367.
DOI Google Scholar
[53]

Bues, J.; Biočanin, M.; Pezoldt, J.; Dainese, R.; Chrisnandy, A.; Rezakhani, S.; Saelens, W.; Gardeux, V.; Gupta, R.; Sarkis, R. et al. Deterministic scRNA-seq captures variation in intestinal crypt and organoid composition. Nat. Methods 2022, 19, 323–330.
DOI Google Scholar
[54]

Cao, J. Y.; Packer, J. S.; Ramani, V.; Cusanovich, D. A.; Huynh, C.; Daza, R.; Qiu, X. J.; Lee, C.; Furlan, S. N.; Steemers, F. J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 2017, 357, 661–667.
DOI Google Scholar
[55]

Datlinger, P.; Rendeiro, A. F.; Boenke, T.; Senekowitsch, M.; Krausgruber, T.; Barreca, D.; Bock, C. Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing. Nat. Methods 2021, 18, 635–642.
DOI Google Scholar
[56]

Zhang, M. X.; Zou, Y.; Xu, X.; Zhang, X. B.; Gao, M. X.; Song, J.; Huang, P. F.; Chen, Q.; Zhu, Z.; Lin, W. et al. Highly parallel and efficient single cell mRNA sequencing with paired picoliter chambers. Nat. Commun. 2020, 11, 2118.
DOI Google Scholar
[57]

Zheng, G. X. Y.; Terry, J. M.; Belgrader, P.; Ryvkin, P.; Bent, Z. W.; Wilson, R.; Ziraldo, S. B.; Wheeler, T. D.; McDermott, G. P.; Zhu, J. J. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 2017, 8, 14049.
DOI Google Scholar
[58]

Peterson, V. M.; Zhang, K. X.; Kumar, N.; Wong, J.; Li, L. X.; Wilson, D. C.; Moore, R.; McClanahan, T. K.; Sadekova, S.; Klappenbach, J. A. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 2017, 35, 936–939.
DOI Google Scholar
[59]

Stoeckius, M.; Hafemeister, C.; Stephenson, W.; Houck-Loomis, B.; Chattopadhyay, P. K.; Swerdlow, H.; Satija, R.; Smibert, P. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 2017, 14, 865–868.
DOI Google Scholar
[60]

Leblay, N.; Maity, R.; Barakat, E.; McCulloch, S.; Duggan, P.; Jimenez-Zepeda, V.; Bahlis, N. J.; Neri, P. Cite-seq profiling of T cells in multiple myeloma patients undergoing BCMA targeting CAR-T or bites immunotherapy. Blood 2020, 136, 11–12.
DOI Google Scholar
[61]

Saigusa, R.; Vallejo, J.; Gulati, R.; Suthahar, S. S. A.; Suryawanshi, V.; Alimadadi, A.; Makings, J.; Durant, C. P.; Freuchet, A.; Roy, P. et al. Sex differences in coronary artery disease and diabetes revealed by scRNA-seq and CITE-seq of human CD4+ T cells. Int. J. Mol. Sci. 2022, 23, 9875.
DOI Google Scholar
[62]

Saigusa, R.; Ley, K. CITE-Seq hits vascular medicine. Clin. Chem. 2020, 66, 751–753.
DOI Google Scholar
[63]

Cadot, S.; Valle, C.; Tosolini, M.; Pont, F.; Largeaud, L.; Laurent, C.; Fournie, J. J.; Ysebaert, L.; Quillet-Mary, A. Longitudinal CITE-Seq profiling of chronic lymphocytic leukemia during ibrutinib treatment: Evolution of leukemic and immune cells at relapse. Biomark. Res. 2020, 8, 72.
DOI Google Scholar
[64]

Buus, T. B.; Herrera, A.; Ivanova, E.; Mimitou, E.; Cheng, A.; Herati, R. S.; Papagiannakopoulos, T.; Smibert, P.; Odum, N.; Koralov, S. B. Improving oligo-conjugated antibody signal in multimodal single-cell analysis. eLife 2021, 10, e61973.
DOI Google Scholar
[65]

Stoeckius, M.; Zheng, S. W.; Houck-Loomis, B.; Hao, S.; Yeung, B. Z.; Mauck III, W. M.; Smibert, P.; Satija, R. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 2018, 19, 224.
DOI Google Scholar
[66]

Wimmers, F.; Subedi, N.; Van Buuringen, N.; Heister, D.; Vivié, J.; Beeren-Reinieren, I.; Woestenenk, R.; Dolstra, H.; Piruska, A.; Jacobs, J. F. M. et al. Single-cell analysis reveals that stochasticity and paracrine signaling control interferon-alpha production by plasmacytoid dendritic cells. Nat. Commun. 2018, 9, 3317.
DOI Google Scholar
[67]

Chen, Z.; Lu, Y.; Zhang, K. R.; Xiao, Y.; Lu, J.; Fan, R. Multiplexed, sequential secretion analysis of the same single cells reveals distinct effector response dynamics dependent on the initial basal state. Adv. Sci. 2019, 6, 1801361.
DOI Google Scholar
[68]

Uhlén, M.; Karlsson, M. J.; Hober, A.; Svensson, A. S.; Scheffel, J.; Kotol, D.; Zhong, W.; Tebani, A.; Strandberg, L.; Edfors, F. et al. The human secretome. Sci. Signaling 2019, 12, eaaz0274.
DOI Google Scholar
[69]

Wu, T. J.; Womersley, H. J.; Wang, J. R.; Scolnick, J.; Cheow, L. F. Time-resolved assessment of single-cell protein secretion by sequencing. Nat. Methods 2023, 20, 723–734.
DOI Google Scholar
[70]

Zhang, Y.; Tang, Y.; Sun, S.; Wang, Z. H.; Wu, W. J.; Zhao, X. D.; Czajkowsky, D. M.; Li, Y.; Tian, J. H.; Xu, L. et al. Single-cell codetection of metabolic activity, intracellular functional proteins, and genetic mutations from rare circulating tumor cells. Anal. Chem. 2015, 87, 9761–9768.
DOI Google Scholar
[71]

Rivello, F.; Van Buijtenen, E.; Matula, K.; Van Buggenum, J. A. G. L.; Vink, P.; Van Eenennaam, H.; Mulder, K. W.; Huck, W. T. S. Single-cell intracellular epitope and transcript detection reveals signal transduction dynamics. Cells Rep. Methods 2021, 1, 100070.
DOI Google Scholar
[72]

Xu, X.; Zhang, M. X.; Zhang, X. B.; Liu, Y. L.; Cai, L. F.; Zhang, Q. Q.; Chen, Q.; Lin, L.; Lin, S. C.; Song, Y. L. et al. Decoding expression dynamics of protein and transcriptome at the single-cell level in paired picoliter chambers. Anal. Chem. 2022, 94, 8164–8173.
DOI Google Scholar
[73]

Saliba, A. E.; Westermann, A. J.; Gorski, S. A.; Vogel, J. Single-cell RNA-seq: Advances and future challenges. Nucleic Acids Res. 2014, 42, 8845–8860.
DOI Google Scholar
[74]

Grindberg, R. V.; Yee-Greenbaum, J. L.; McConnell, M. J.; Novotny, M.; O’Shaughnessy, A. L.; Lambert, G. M.; Araúzo-Bravo, M. J.; Lee, J.; Fishman, M.; Robbins, G. E. et al. RNA-sequencing from single nuclei. Proc. Natl. Acad. Sci. USA 2013, 110, 19802–19807.
DOI Google Scholar
[75]

Habib, N.; Avraham-Davidi, I.; Basu, A.; Burks, T.; Shekhar, K.; Hofree, M.; Choudhury, S. R.; Aguet, F.; Gelfand, E.; Ardlie, K. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 2017, 14, 955–958.
DOI Google Scholar
[76]

Huang, H. L.; Hsing, H. W.; Lai, T. C.; Chen, Y. W.; Lee, T. R.; Chan, H. T.; Lyu, P. C.; Wu, C. L.; Lu, Y. C.; Lin, S. T. et al. Trypsin-induced proteome alteration during cell subculture in mammalian cells. J. Biomed. Sci. 2010, 17, 36.
DOI Google Scholar
[77]

Lake, B. B.; Chen, S.; Sos, B. C.; Fan, J.; Kaeser, G. E.; Yung, Y. C.; Duong, T. E.; Gao, D.; Chun, J.; Kharchenko, P. V. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 2018, 36, 70–80.
DOI Google Scholar
[78]

Bakken, T. E.; Hodge, R. D.; Miller, J. A.; Yao, Z. Z.; Nguyen, T. N.; Aevermann, B.; Barkan, E.; Bertagnolli, D.; Casper, T.; Dee, N. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS One 2018, 13, e0209648.
DOI Google Scholar
[79]

Lake, B. B.; Ai, R.; Kaeser, G. E.; Salathia, N. S.; Yung, Y. C.; Liu, R.; Wildberg, A.; Gao, D.; Fung, H. L.; Chen, S. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 2016, 352, 1586–1590.
DOI Google Scholar
[80]

Fischer, J.; Ayers, T. Single nucleus RNA-sequencing: How it’s done, applications and limitations. Emerg. Top. Life Sci. 2021, 5, 687–690.
DOI Google Scholar
[81]

Chung, H.; Parkhurst, C. N.; Magee, E. M.; Phillips, D.; Habibi, E.; Chen, F.; Yeung, B. Z.; Waldman, J.; Artis, D.; Regev, A. Joint single-cell measurements of nuclear proteins and RNA in vivo. Nat. Methods 2021, 18, 1204–1212.
DOI Google Scholar
[82]

Mair, F.; Erickson, J. R.; Voillet, V.; Simoni, Y.; Bi, T.; Tyznik, A. J.; Martin, J.; Gottardo, R.; Newell, E. W.; Prlic, M. A targeted multi-omic analysis approach measures protein expression and low-abundance transcripts on the single-cell level. Cell Rep. 2020, 31, 107499.
DOI Google Scholar
[83]

Gerlach, J. P.; Van Buggenum, J. A. G.; Tanis, S. E. J.; Hogeweg, M.; Heuts, B. M. H.; Muraro, M. J.; Elze, L.; Rivello, F.; Rakszewska, A.; Van Oudenaarden, A. et al. Combined quantification of intracellular (phospho-)proteins and transcriptomics from fixed single cells. Sci. Rep. 2019, 9, 1469.
DOI Google Scholar
[84]

Hashimshony, T.; Senderovich, N.; Avital, G.; Klochendler, A.; De Leeuw, Y.; Anavy, L.; Gennert, D.; Li, S. Q.; Livak, K. J.; Rozenblatt-Rosen, O. et al. CEL-Seq2: Sensitive highly-multiplexed single-cell RNA-Seq. Genome. Biol. 2016, 17, 77.
DOI Google Scholar
[85]

O'Huallachain, M.; Bava, F. A.; Shen, M.; Dallett, C.; Paladugu, S.; Samusik, N.; Yu, S.; Hussein, R.; Hillman, G. R.; Higgins, S. et al. Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis. Commun. Biol. 2020, 3, 213.
DOI Google Scholar
[86]

Hwang, B.; Lee, D. S.; Tamaki, W.; Sun, Y.; Ogorodnikov, A.; Hartoularos, G. C.; Winters, A.; Yeung, B. Z.; Nazor, K. L.; Song, Y. S. et al. SCITO-seq: Single-cell combinatorial indexed cytometry sequencing. Nat. Methods 2021, 18, 903–911.
DOI Google Scholar
[87]

Rouhanifard, S. H.; Mellis, I. A.; Dunagin, M.; Bayatpour, S.; Jiang, C. L.; Dardani, I.; Symmons, O.; Emert, B.; Torre, E.; Cote, A. et al. ClampFISH detects individual nucleic acid molecules using click chemistry-based amplification. Nat. Biotechnol. 2019, 37, 84–89.
DOI Google Scholar
[88]

Alles, J.; Karaiskos, N.; Praktiknjo, S. D.; Grosswendt, S.; Wahle, P.; Ruffault, P. L.; Ayoub, S.; Schreyer, L.; Boltengagen, A.; Birchmeier, C. et al. Cell fixation and preservation for droplet-based single-cell transcriptomics. BMC Biol. 2017, 15, 44.
DOI Google Scholar
[89]

Aydin, S. A short history, principles, and types of ELISA, and our laboratory experience with peptide/protein analyses using ELISA. Peptides 2015, 72, 4–15.
DOI Google Scholar
[90]

Asensio, L.; González, I.; García, T.; Martín, R. Determination of food authenticity by enzyme-linked immunosorbent assay (ELISA). Food Control 2008, 19, 1–8.
DOI Google Scholar
[91]

Xu, A. M.; Liu, Q. H.; Takata, K. L.; Jeoung, S.; Su, Y. P.; Antoshechkin, I.; Chen, S. S.; Thomson, M.; Heath, J. R. Integrated measurement of intracellular proteins and transcripts in single cells. Lab Chip 2018, 18, 3251–3262.
DOI Google Scholar
[92]

George, J.; Wang, J. Assay of genome-wide transcriptome and secreted proteins on the same single immune cells by microfluidics and RNA sequencing. Anal. Chem. 2016, 88, 10309–10315.
DOI Google Scholar
[93]

Fulwyler, M. J. Electronic separation of biological cells by volume. Science 1965, 150, 910–911.
DOI Google Scholar
[94]

Spitzer, M. H.; Nolan, G. P. Mass cytometry: Single cells, many features. Cell 2016, 165, 780–791.
DOI Google Scholar
[95]

Iyer, A.; Hamers, A. A. J.; Pillai, A. B. CyTOF® for the masses. Front. Immunol. 2022, 13, 815828.
DOI Google Scholar
[96]

Jaitin, D. A.; Kenigsberg, E.; Keren-Shaul, H.; Elefant, N.; Paul, F.; Zaretsky, I.; Mildner, A.; Cohen, N.; Jung, S.; Tanay, A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 2014, 343, 776–779.
DOI Google Scholar
[97]

Katzenelenbogen, Y.; Sheban, F.; Yalin, A.; Yofe, I.; Svetlichnyy, D.; Jaitin, D. A.; Bornstein, C.; Moshe, A.; Keren-Shaul, H.; Cohen, M. et al. Coupled scRNA-seq and intracellular protein activity reveal an immunosuppressive role of TREM2 in cancer. Cell 2020, 182, 872–885.e19.
DOI Google Scholar
[98]

Gubin, M. M.; Esaulova, E.; Ward, J. P.; Malkova, O. N.; Runci, D.; Wong, P.; Noguchi, T.; Arthur, C. D.; Meng, W.; Alspach, E. et al. High-dimensional analysis delineates myeloid and lymphoid compartment remodeling during successful immune-checkpoint cancer therapy. Cell 2018, 175, 1014–1030.e19.
DOI Google Scholar
[99]

Giordani, L.; He, G. J.; Negroni, E.; Sakai, H.; Law, J. Y. C.; Siu, M. M.; Wan, R.; Corneau, A.; Tajbakhsh, S.; Cheung, T. H. et al. High-dimensional single-cell cartography reveals novel skeletal muscle-resident cell populations. Mol. Cell 2019, 74, 609–621.e6.
DOI Google Scholar
[100]

Paul, F.; Arkin, Y.; Giladi, A.; Jaitin, D. A.; Kenigsberg, E.; Keren-Shaul, H.; Winter, D.; Lara-Astiaso, D.; Gury, M.; Weiner, A. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 2015, 163, 1663–1677.
DOI Google Scholar
[101]

Fredriksson, S.; Gullberg, M.; Jarvius, J.; Olsson, C.; Pietras, K.; Gústafsdóttir, S. M.; Östman, A.; Landegren, U. Protein detection using proximity-dependent DNA ligation assays. Nat. Biotechnol. 2002, 20, 473–477.
DOI Google Scholar
[102]

Lundberg, M.; Eriksson, A.; Tran, B.; Assarsson, E.; Fredriksson, S. Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood. Nucleic Acids Res. 2011, 39, e102.
DOI Google Scholar
[103]

Gullberg, M.; Gústafsdóttir, S. M.; Schallmeiner, E.; Jarvius, J.; Bjarnegård, M.; Betsholtz, C.; Landegren, U.; Fredriksson, S. Cytokine detection by antibody-based proximity ligation. Proc. Natl. Acad. Sci. USA 2004, 101, 8420–8424.
DOI Google Scholar
[104]

Weibrecht, I.; Lundin, E.; Kiflemariam, S.; Mignardi, M.; Grundberg, I.; Larsson, C.; Koos, B.; Nilsson, M.; Söderberg, O. In situ detection of individual mRNA molecules and protein complexes or post-translational modifications using padlock probes combined with the in situ proximity ligation assay. Nat. Protoc. 2013, 8, 355–372.
DOI Google Scholar
[105]

Taylor, S.; Wakem, M.; Dijkman, G.; Alsarraj, M.; Nguyen, M. A practical approach to RT-qPCR-publishing data that conform to the MIQE guidelines. Methods 2010, 50, S1–S5.
DOI Google Scholar
[106]

Pabinger, S.; Rödiger, S.; Kriegner, A.; Vierlinger, K.; Weinhäusel, A. A survey of tools for the analysis of quantitative PCR (qPCR) data. Biomol. Detect. Quantif. 2014, 1, 23–33.
DOI Google Scholar
[107]

Nolan, T.; Hands, R. E.; Bustin, S. A. Quantification of mRNA using real-time RT-PCR. Nat. Protoc. 2006, 1, 1559–1582.
DOI Google Scholar
[108]

Darmanis, S.; Gallant, C. J.; Marinescu, V. D.; Niklasson, M.; Segerman, A.; Flamourakis, G.; Fredriksson, S.; Assarsson, E.; Lundberg, M.; Nelander, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 2016, 14, 380–389.
DOI Google Scholar
[109]

Genshaft, A. S.; Li, S. Q.; Gallant, C. J.; Darmanis, S.; Prakadan, S. M.; Ziegler, C. G. K.; Lundberg, M.; Fredriksson, S.; Hong, J.; Regev, A. et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 2016, 17, 188.
DOI Google Scholar
[110]

Ståhlberg, A.; Thomsen, C.; Ruff, D.; Åman, P. Quantitative PCR analysis of DNA, RNAs, and proteins in the same single cell. Clin. Chem. 2012, 58, 1682–1691.
DOI Google Scholar
[111]

Albayrak, C.; Jordi, C. A.; Zechner, C.; Lin, J.; Bichsel, C. A.; Khammash, M.; Tay, S. Digital quantification of proteins and mRNA in single mammalian cells. Mol. Cell 2016, 61, 914–924.
DOI Google Scholar
[112]

Lin, J.; Jordi, C.; Son, M.; Van Phan, H.; Drayman, N.; Abasiyanik, M. F.; Vistain, L.; Tu, H. L.; Tay, S. Ultra-sensitive digital quantification of proteins and mRNA in single cells. Nat. Commun. 2019, 10, 3544.
DOI Google Scholar
[113]

Hindson, C. M.; Chevillet, J. R.; Briggs, H. A.; Gallichotte, E. N.; Ruf, I. K.; Hindson, B. J.; Vessella, R. L.; Tewari, M. Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat. Methods 2013, 10, 1003–1005.
DOI Google Scholar
[114]

Hindson, B. J.; Ness, K. D.; Masquelier, D. A.; Belgrader, P.; Heredia, N. J.; Makarewicz, A. J.; Bright, I. J.; Lucero, M. Y.; Hiddessen, A. L.; Legler, T. C. et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 2011, 83, 8604–8610.
DOI Google Scholar
[115]

Reimegård, J.; Tarbier, M.; Danielsson, M.; Schuster, J.; Baskaran, S.; Panagiotou, S.; Dahl, N.; Friedländer, M. R.; Gallant, C. J. A combined approach for single-cell mRNA and intracellular protein expression analysis. Commun. Biol. 2021, 4, 624.
DOI Google Scholar
[116]

Vistain, L.; Van Phan, H.; Keisham, B.; Jordi, C.; Chen, M. J.; Reddy, S. T.; Tay, S. Quantification of extracellular proteins, protein complexes and mRNAs in single cells by proximity sequencing. Nat. Methods 2022, 19, 1578–1589.
DOI Google Scholar
[117]

Barbieri, I.; Kouzarides, T. Role of RNA modifications in cancer. Nat. Rev. Cancer 2020, 20, 303–322.
DOI Google Scholar
[118]

Roundtree, I. A.; Evans, M. E.; Pan, T.; He, C. Dynamic RNA modifications in gene expression regulation. Cell 2017, 169, 1187–1200.
DOI Google Scholar
[119]

Minguez, P.; Letunic, I.; Parca, L.; Bork, P. PTMcode: A database of known and predicted functional associations between post-translational modifications in proteins. Nucleic Acids Res. 2013, 41, D306–D311.
DOI Google Scholar
[120]

Mimitou, E. P.; Lareau, C. A.; Chen, K. Y.; Zorzetto-Fernandes, A. L.; Hao, Y. H.; Takeshima, Y.; Luo, W.; Huang, T. S.; Yeung, B. Z.; Papalexi, E. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. 2021, 39, 1246–1258.
DOI Google Scholar
[121]

Swanson, E.; Lord, C.; Reading, J.; Heubeck, A. T.; Genge, P. C.; Thomson, Z.; Weiss, M. D. A.; Li, X. J.; Savage, A. K.; Green, R. R. et al. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. eLife 2021, 10, e63632.
DOI Google Scholar
[122]

Chen, A. F.; Parks, B.; Kathiria, A. S.; Ober-Reynolds, B.; Goronzy, J. J.; Greenleaf, W. J. NEAT-seq: Simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells. Nat. Methods 2022, 19, 547–553.
DOI Google Scholar
[123]

Kukurba, K. R.; Montgomery, S. B. RNA sequencing and analysis. Cold Spring Harb Protoc. 2015, 2015, 951–969.
DOI Google Scholar
[124]

Cao, Z. J.; Gao, G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat. Biotechnol. 2022, 40, 1458–1466.
DOI Google Scholar
[125]

Van Vliet, S.; Dal Co, A.; Winkler, A. R.; Spriewald, S.; Stecher, B.; Ackermann, M. Spatially correlated gene expression in bacterial groups: The role of lineage history, spatial gradients, and cell–cell interactions. Cell Syst. 2018, 6, 496–507.e6.
DOI Google Scholar
[126]

Scadden, D. T. Nice neighborhood: Emerging concepts of the stem cell niche. Cell 2014, 157, 41–50.
DOI Google Scholar
[127]

Lavin, Y.; Winter, D.; Blecher-Gonen, R.; David, E.; Keren-Shaul, H.; Merad, M.; Jung, S.; Amit, I. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 2014, 159, 1312–1326.
DOI Google Scholar
[128]

Vickovic, S.; Eraslan, G.; Salmén, F.; Klughammer, J.; Stenbeck, L.; Schapiro, D.; Äijö, T.; Bonneau, R.; Bergenstråhle, L.; Navarro, J. F. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 2019, 16, 987–990.
DOI Google Scholar
[129]

Ståhl, P. L.; Salmén, F.; Vickovic, S.; Lundmark, A.; Navarro, J. F.; Magnusson, J.; Giacomello, S.; Asp, M.; Westholm, J. O.; Huss, M. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 2016, 353, 78–82.
DOI Google Scholar
[130]

Rodriques, S. G.; Stickels, R. R.; Goeva, A.; Martin, C. A.; Murray, E.; Vanderburg, C. R.; Welch, J.; Chen, L. M.; Chen, F.; Macosko, E. Z. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 2019, 363, 1463–1467.
DOI Google Scholar
[131]

Xing, Q. R.; Cipta, N. O.; Hamashima, K.; Liou, Y. C.; Koh, C. G.; Loh, Y. H. Unraveling heterogeneity in transcriptome and its regulation through single-cell multi-omics technologies. Front. Genet. 2020, 11, 662.
DOI Google Scholar
[132]

Vickovic, S.; Lötstedt, B.; Klughammer, J.; Mages, S.; Segerstolpe, Å.; Rozenblatt-Rosen, O.; Regev, A. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat. Commun. 2022, 13, 795.
DOI Google Scholar
[133]

Liu, Y.; Yang, M. Y.; Deng, Y. X.; Su, G.; Enninful, A.; Guo, C. C.; Tebaldi, T.; Zhang, D.; Kim, D.; Bai, Z. L. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 2020, 183, 1665–1681.e18.
DOI Google Scholar
Publication history
Copyright
Acknowledgements

Publication history

Received: 05 May 2023
Revised: 11 June 2023
Accepted: 12 June 2023
Published: 01 August 2023
Issue date: January 2024

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgements

We thank the National Natural Science Foundation of China (Nos. 22293031, 22004083, 21927806, 82227801, and 82341023), the National Key R&D Program of China (No. 2019YFA0905800), and the Innovative research team of high-level local universities in Shanghai (No. SHSMU-ZLCX20212601) for their financial support.

Return