A compendium of single cell analysis in aging and disease

Uday Chintapula; Samir M Iqbal; Young-Tae Kim; Uday Chintapula; Samir M Iqbal; Young-Tae Kim

doi:10.3934/molsci.2020004

AIMS Molecular Science

2020, Volume 7, Issue 1: 49-69. doi: 10.3934/molsci.2020004

Previous Article Next Article

Review

A compendium of single cell analysis in aging and disease

1.
Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA
2.
Joint Bioengineering Program, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
3.
Nano-bio Lab, Department of Electrical Engineering, and School of Medicine, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA
4.
Department of Urology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75235, USA

Received: 14 August 2019 Accepted: 25 March 2020 Published: 27 March 2020

Cell is the fundamental structural and functional unit of complex multicellular organisms. Conventional methods which involve average analysis of cells in bulk populations can undermine physiologically significant cell populations, whereas analysis of cells at a single cell level may reveal unique biomarkers and other mechanisms that govern the genotype and phenotype in various physiological processes in presumed homogenous cell populations. Cellular abnormalities such as irregularities in cellular mechanisms have been linked to human aging and other major diseases including neurodegenerative, vascular, autoimmune, and cancer. Aging is a functional decline associated with various diseases in an organism, majorly arising from cellular abnormalities. Single cell analysis (SCA) which involves isolation and study of single cell proteomics, genomics, transcriptomics and metabolomics which enables research of cellular abnormalities with a molecular resolution, is gaining recognition in the research of human aging and disease. The advances in SCA are producing breakthrough information about cellular heterogeneity, disease progression, cellular microenvironment and its interactions, early diagnostics, improving precision medicine through high throughput drug screening and discovery of novel biomarkers; combinedly, these advances exhibit the potential of SCA to study of human aging and disease. Primarily, we review the role of SCA in understanding cellular mechanisms involved in aging and other major diseases including neurological, vascular, autoimmunity and cancer. Secondly, we also include review of SCA role in studying cell adhesion mechanisms which are involved in tissue development and maintenance and disease progression. Finally, SCA potential to empower precision medicine and its overall challenges along with future directions are discussed.
- single cell analysis,
- aging,
- single cell RNA-seq,
- disease,
- adhesion,
- cancer
Citation: Uday Chintapula, Samir M Iqbal, Young-Tae Kim. A compendium of single cell analysis in aging and disease[J]. AIMS Molecular Science, 2020, 7(1): 49-69. doi: 10.3934/molsci.2020004

Related Papers:

Abstract

Cell is the fundamental structural and functional unit of complex multicellular organisms. Conventional methods which involve average analysis of cells in bulk populations can undermine physiologically significant cell populations, whereas analysis of cells at a single cell level may reveal unique biomarkers and other mechanisms that govern the genotype and phenotype in various physiological processes in presumed homogenous cell populations. Cellular abnormalities such as irregularities in cellular mechanisms have been linked to human aging and other major diseases including neurodegenerative, vascular, autoimmune, and cancer. Aging is a functional decline associated with various diseases in an organism, majorly arising from cellular abnormalities. Single cell analysis (SCA) which involves isolation and study of single cell proteomics, genomics, transcriptomics and metabolomics which enables research of cellular abnormalities with a molecular resolution, is gaining recognition in the research of human aging and disease. The advances in SCA are producing breakthrough information about cellular heterogeneity, disease progression, cellular microenvironment and its interactions, early diagnostics, improving precision medicine through high throughput drug screening and discovery of novel biomarkers; combinedly, these advances exhibit the potential of SCA to study of human aging and disease. Primarily, we review the role of SCA in understanding cellular mechanisms involved in aging and other major diseases including neurological, vascular, autoimmunity and cancer. Secondly, we also include review of SCA role in studying cell adhesion mechanisms which are involved in tissue development and maintenance and disease progression. Finally, SCA potential to empower precision medicine and its overall challenges along with future directions are discussed.

Acknowledgments

Graphical figures created using Biorender.

Conflict of interest

The authors declare no conflict of interest for the contributions in this manuscript.

References

[1]	Armbrecht L, Dittrich PS (2017) Recent Advances in the Analysis of Single Cells. Anal Chem 89: 2-21. doi: 10.1021/acs.analchem.6b04255
[2]	Al Amir Dache Z, Otandault A, Tanos R, et al. (2020) Blood contains circulating cell-free respiratory competent mitochondria. FASEB J 34: 3616-3630. doi: 10.1096/fj.201901917RR
[3]	Tritschler S, Theis FJ, Lickert H, et al. (2017) Systematic single-cell analysis provides new insights into heterogeneity and plasticity of the pancreas. Mol Metab 6: 974-990. doi: 10.1016/j.molmet.2017.06.021
[4]	Ryan FP (2016) Viral symbiosis and the holobiontic nature of the human genome. APMIS 124: 11-19. doi: 10.1111/apm.12488
[5]	Dimijian GG (2000) Evolving together: the biology of symbiosis, part 2. Proc (Bayl Univ Med Cent) 13: 381-390. doi: 10.1080/08998280.2000.11927712
[6]	Hofmeyr JHS (2008) The harmony of the cell: cellular processes. Essays Biochem 45: 57-66. doi: 10.1042/bse0450057
[7]	Lane N, Martin W (2010) The energetics of genome complexity. Nature 467: 929-934. doi: 10.1038/nature09486
[8]	Wallace DC (2007) Why Do We Still Have a Maternally Inherited Mitochondrial DNA? Insights from Evolutionary Medicine. Annu Rev Biochem 76: 781-821. doi: 10.1146/annurev.biochem.76.081205.150955
[9]	Mojtahedi M, Skupin A, Zhou J, et al. (2016) Cell Fate Decision as High-Dimensional Critical State Transition. PLoS Biol 14: 1-29. doi: 10.1371/journal.pbio.2000640
[10]	From M, Hematopoietic P (2015) Brief Report: Single-Cell Analysis Reveals Cell Division-Independent Emergence of Stem Cells. Stem Cells 33: 3152-3157. doi: 10.1002/stem.2106
[11]	Kolodziejczyk AA, Kim JK, Svensson V, et al. (2015) The Technology and Biology of Single-Cell RNA Sequencing. Mol Cell 58: 610-620. doi: 10.1016/j.molcel.2015.04.005
[12]	Ziegenhain C, Vieth B, Parekh S, et al. (2017) Comparative Analysis of Single-Cell RNA Sequencing Methods. Mol Cell 65: 631-643.e4. doi: 10.1016/j.molcel.2017.01.023
[13]	Rosenberg AB, Roco CM, Muscat RA, et al. (2018) Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360: 176-182. doi: 10.1126/science.aam8999
[14]	Lee BWL, Ghode P, Ong DST (2019) Redox regulation of cell state and fate. Redox Biol 25: 101056. doi: 10.1016/j.redox.2018.11.014
[15]	Trapnell C (2015) Defining cell types and states with single-cell genomics. Genome Res 25: 1491-1498. doi: 10.1101/gr.190595.115
[16]	MacLean AL, Hong T, Nie Q (2018) Exploring intermediate cell states through the lens of single cells. Curr Opin Syst Biol 9: 32-41. doi: 10.1016/j.coisb.2018.02.009
[17]	Hu P, Zhang W, Xin H, et al. (2016) Single Cell Isolation and Analysis. Front Cell Dev Biol 4: 1-12.
[18]	Armbrecht L, Dittrich PS (2017) Recent Advances in the Analysis of Single Cells. Anal Chem 89: 2-21. doi: 10.1021/acs.analchem.6b04255
[19]	Bengtsson M, Ståhlberg A, Rorsman P, et al. (2005) Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res 15: 1388-1392. doi: 10.1101/gr.3820805
[20]	Wang J, Min Z, Jin M, et al. (2015) Protocol for Single Cell Isolation by Flow Cytometry BT- Single Cell Sequencing and Systems Immunology. Single Cell Sequencing and Systems Immunology, Translational Bioinformatics Dordrecht: Springer, 155-163. doi: 10.1007/978-94-017-9753-5_11
[21]	Espina V, Wulfkuhle JD, Calvert VS, et al. (2006) Laser-capture microdissection. Nat Protoc 1: 586-603. doi: 10.1038/nprot.2006.85
[22]	Reece A, Xia B, Jiang Z, et al. (2016) Microfluidic techniques for high throughput single cell analysis. Curr Opin Biotechnol 40: 90-96. doi: 10.1016/j.copbio.2016.02.015
[23]	Torres AJ, Hill AS, Love JC (2014) Nanowell-based immunoassays for measuring single-cell secretion: characterization of transport and surface binding. Anal Chem 86: 11562-11569. doi: 10.1021/ac4030297
[24]	Islam M, Sajid A, Mahmood MAI, et al. (2015) Nanotextured polymer substrates show enhanced cancer cell isolation and cell culture. Nanotechnology 26: 225101. doi: 10.1088/0957-4484/26/22/225101
[25]	Fang T, Shang W, Liu C, et al. (2019) Nondestructive Identification and Accurate Isolation of Single Cells through a Chip with Raman Optical Tweezers. Anal Chem 91: 9932-9939. doi: 10.1021/acs.analchem.9b01604
[26]	Muraro MJ, Dharmadhikari G, Grün D, et al. (2016) A Single-Cell Transcriptome Atlas of the Human Pancreas. Cell Syst 3: 385-394.e3. doi: 10.1016/j.cels.2016.09.002
[27]	Ji Y, Qi D, Li L, et al. (2019) Multiplexed profiling of single-cell extracellular vesicles secretion. Proc Natl Acad Sci 116: 5979-5984. doi: 10.1073/pnas.1814348116
[28]	Yeo T, Tan SJ, Lim CL, et al. (2016) Microfluidic enrichment for the single cell analysis of circulating tumor cells. Sci Rep 6: 22076. doi: 10.1038/srep22076
[29]	Yuan J, Sheng J, Sims PA (2018) SCOPE-Seq: a scalable technology for linking live cell imaging and single-cell RNA sequencing. Genome Biol 19: 227. doi: 10.1186/s13059-018-1607-x
[30]	Ettinger A, Wittmann T (2014) Fluorescence live cell imaging. Methods Cell Biol 123: 77-94. doi: 10.1016/B978-0-12-420138-5.00005-7
[31]	Ayan B, Ozcelik A, Bachman H, et al. (2016) Acoustofluidic coating of particles and cells. Lab Chip 16: 4366-4372. doi: 10.1039/C6LC00951D
[32]	Johnson BN, Mutharasan R (2016) Acoustofluidic particle trapping, manipulation, and release using dynamic-mode cantilever sensors. Analyst Dec 142: 123-131. doi: 10.1039/C6AN01743F
[33]	Mao Z, Li P, Wu M, et al. (2017) Enriching Nanoparticles via Acoustofluidics. ACS Nano 11: 603-612. doi: 10.1021/acsnano.6b06784
[34]	Acero Sanchez JL, Joda H, Henry OYF, et al. (2017) Electrochemical Genetic Profiling of Single Cancer Cells. Anal Chem 89: 3378-3385. doi: 10.1021/acs.analchem.6b03973
[35]	Long D, Shang Y, Qiu Y, et al. (2018) A single-cell analysis platform for electrochemiluminescent detection of platelets adhesion to endothelial cells based on Au@DL-ZnCQDs nanoprobes. Biosens Bioelectron 102: 553-559. doi: 10.1016/j.bios.2017.11.058
[36]	Zhang J, Zhou J, Pan R, et al. (2018) New Frontiers and Challenges for Single-Cell Electrochemical Analysis. ACS Sens 3: 242-250. doi: 10.1021/acssensors.7b00711
[37]	Yang W, Tu Z, Wang H, et al. (2018) The mechanism of reduced IgG/IgE-binding of beta-lactoglobulin by pulsed electric field pretreatment combined with glycation revealed by ECD/FTICR-MS. Food Funct 9: 417-425. doi: 10.1039/C7FO01082F
[38]	Umar A, Jaremko M, Burgers PC, et al. (2008) High-throughput proteomics of breast carcinoma cells: a focus on FTICR-MS. Expert Rev Proteomics 5: 445-455. doi: 10.1586/14789450.5.3.445
[39]	Tosevski V, Ulashchik E, Trovato A, et al. (2017) CyTOF Mass Cytometry for Click Proliferation Assays. Curr Protoc Cytom 81: 7.50.1-7.50.14.
[40]	Fletcher JS, Rabbani S, Henderson A, et al. (2008) A new dynamic in mass spectral imaging of single biological cells. Anal Chem 80: 9058-9064. doi: 10.1021/ac8015278
[41]	Shen Y, Tolic N, Masselon C, et al. (2004) Ultrasensitive proteomics using high-efficiency on-line micro-SPE-nanoLC-nanoESI MS and MS/MS. Anal Chem 76: 144-154. doi: 10.1021/ac030096q
[42]	VanInsberghe M, Zahn H, White AK, et al. (2018) Highly multiplexed single-cell quantitative PCR. PLoS One 13: e0191601. doi: 10.1371/journal.pone.0191601
[43]	Chen J, Xu Y, Shi Y, et al. (2019) Functionalization of Atomic Force Microscope Cantilevers with Single-T Cells or Single-Particle for Immunological Single-Cell Force Spectroscopy. J Vis Exp e59609.
[44]	Lulevich V, Zink T, Chen HY, et al. (2006) Cell Mechanics Using Atomic Force Microscopy-Based Single-Cell Compression. Langmuir 22: 8151-8155. doi: 10.1021/la060561p
[45]	Balasubramanian S, Kagan D, Hu CMJ, et al. (2011) Micromachine-enabled capture and isolation of cancer cells in complex media. Angew Chem Int Ed Engl 50: 4161-4164. doi: 10.1002/anie.201100115
[46]	Esteban-Fernandez de Avila B, Martin A, Soto F, et al. (2015) Single Cell Real-Time miRNAs Sensing Based on Nanomotors. ACS Nano 9: 6756-6764. doi: 10.1021/acsnano.5b02807
[47]	Zhang Y, Jin L, Xu J, et al. (2017) Dynamic characterization of drug resistance and heterogeneity of the gastric cancer cell BGC823 using single-cell Raman spectroscopy. Analyst 143: 164-174. doi: 10.1039/C7AN01287J
[48]	Franco D, Trusso S, Fazio E, et al. (2017) Raman spectroscopy differentiates between sensitive and resistant multiple myeloma cell lines. Spectrochim Acta A Mol Biomol Spectrosc 187: 15-22. doi: 10.1016/j.saa.2017.06.020
[49]	Bayani J, Squire JA (2004) Fluorescence in situ Hybridization (FISH). Curr Protoc cell Biol Chapter 22: Unit 22.4.
[50]	Yurov YB, Vostrikov VM, Vorsanova SG, et al. (2001) Multicolor fluorescent in situ hybridization on post-mortem brain in schizophrenia as an approach for identification of low-level chromosomal aneuploidy in neuropsychiatric diseases. Brain Dev 23: 186-190. doi: 10.1016/S0387-7604(01)00363-1
[51]	Querido E, Dekakra-Bellili L, Chartrand P (2017) RNA fluorescence in situ hybridization for high-content screening. Methods 126: 149-155. doi: 10.1016/j.ymeth.2017.07.005
[52]	Ravindranathan A, Diolaiti ME, Cimini BA, et al. (2019) In Situ Visualization of Telomere Length, Telomere Elongation, and TERT Expression in Single Cells. Curr Protoc Cell Biol 85: e97. doi: 10.1002/cpcb.97
[53]	Habib N, Li Y, Heidenreich M, et al. (2016) Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353: 925-928. doi: 10.1126/science.aad7038
[54]	Spaethling JM, Na YJ, Lee J, et al. (2017) Primary Cell Culture of Live Neurosurgically Resected Aged Adult Human Brain Cells and Single Cell Transcriptomics. Cell Rep 18: 791-803. doi: 10.1016/j.celrep.2016.12.066
[55]	Morita Y, Ema H, Nakauchi H (2010) Heterogeneity and hierarchy within the most primitive hematopoietic stem cell compartment. J Exp Med 207: 1173-1182. doi: 10.1084/jem.20091318
[56]	Sarkar S, Motwani V, Sabhachandani P, et al. (2015) T Cell Dynamic Activation and Functional Analysis in Nanoliter Droplet Microarray. J Clin Cell Immunol 6: 334. doi: 10.4172/2155-9899.1000334
[57]	Ludwig LS, Lareau CA, Ulirsch JC, et al. (2019) Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics. Cell 176: 1325-1339.e22. doi: 10.1016/j.cell.2019.01.022
[58]	Mishra P, Martin DC, Androulakis IP, et al. (2019) Fluorescence Imaging of Actin Turnover Parses Early Stem Cell Lineage Divergence and Senescence. Sci Rep 9: 10377. doi: 10.1038/s41598-019-46682-y
[59]	Mansur N, Hasan MR, Kim Y, et al. (2017) Functionalization of nanotextured substrates for enhanced identification of metastatic breast cancer cells. Nanotechnology 28: 385101. doi: 10.1088/1361-6528/aa7f84
[60]	Nguyen AT, Sathe SR, Yim EKF (2016) From nano to micro: topographical scale and its impact on cell adhesion, morphology and contact guidance. J Phys Condens Matter 28: 183001. doi: 10.1088/0953-8984/28/18/183001
[61]	Palmer CP, Mycielska ME, Burcu H, et al. (2008) Single cell adhesion measuring apparatus (SCAMA): Application to cancer cell lines of different metastatic potential and voltage-gated Na+ channel expression. Eur Biophys J 37: 359-368. doi: 10.1007/s00249-007-0219-2
[62]	Dong H, Sun H, Zheng J (2016) A microchip for integrated single-cell genotoxicity assay. Talanta 161: 804-811. doi: 10.1016/j.talanta.2016.09.040
[63]	Du Y, Li N, Yang H, et al. (2017) Mimicking Liver Sinusoidal Structures and Functions using a 3D-configured Microfluidic Chip. Lab Chip 17-20.
[64]	Kaminski TS, Scheler O, Garstecki P (2016) Droplet microfluidics for microbiology: techniques, applications and challenges. Lab Chip 16: 2168-2187. doi: 10.1039/C6LC00367B
[65]	Gawel DR, Serra-Musach J, Lilja S, et al. (2019) A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases. Genome Med 11: 47. doi: 10.1186/s13073-019-0657-3
[66]	Vaux DL, Haecker G, Strasser A (1994) An Evolutionary on Apoptosis Perspective Minireview. Cell 76: 777-779. doi: 10.1016/0092-8674(94)90350-6
[67]	Kowalczyk MS, Tirosh I, Heckl D, et al. (2015) Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res 25: 1860-1872. doi: 10.1101/gr.192237.115
[68]	Apple DM, Solano-Fonseca R, Kokovay E (2017) Neurogenesis in the aging brain. Biochem Pharmacol 141: 77-85. doi: 10.1016/j.bcp.2017.06.116
[69]	Dulken BW, Buckley MT, Navarro Negredo P, et al. (2019) Single-cell analysis reveals T cell infiltration in old neurogenic niches. Nature 571: 205-210. doi: 10.1038/s41586-019-1362-5
[70]	Ximerakis M, Lipnick SL, Simmons SK, et al. (2018) Single-cell transcriptomics of the aged mouse brain reveals convergent, divergent and unique aging signatures. bioRxiv 440032.
[71]	Zhang Y, Kim MS, Jia B, et al. (2017) Hypothalamic stem cells control ageing speed partly through exosomal miRNAs. Nature 548: 52-57. doi: 10.1038/nature23282
[72]	Kim Y, Karthikeyan K, Chirvi S, et al. (2009) Neuro-optical microfluidic platform to study injury and regeneration of single axons. Lab Chip 9: 2576-2581. doi: 10.1039/b903720a
[73]	Iourov IY, Vorsanova SG, Yurov YB (2012) Single Cell Genomics of the Brain: Focus on Neuronal Diversity and Neu- ropsychiatric Diseases. Curr Genomics 13: 477-488. doi: 10.2174/138920212802510439
[74]	Graff J, Kim D, Dobbin MM, et al. (2011) Epigenetic Regulation of Gene Expression in Physiological and Pathological Brain Processes. Physiol Rev 91: 603-649. doi: 10.1152/physrev.00012.2010
[75]	Faggioli F, Vijg J, Montagna C (2011) Chromosomal aneuploidy in the aging brain. Mech Ageing Dev 132: 429-436. doi: 10.1016/j.mad.2011.04.008
[76]	Yurov YB, Vorsanova SG, Iourov IY (2009) GIN'n'CIN hypothesis of brain aging: deciphering the role of somatic genetic instabilities and neural aneuploidy during ontogeny. Mol Cytogenet 2: 23. doi: 10.1186/1755-8166-2-23
[77]	Kim DW, Washington PW, Wang ZQ, et al. (2019) Single cell RNA-Seq analysis identifies molecular mechanisms controlling hypothalamic patterning and differentiation. bioRxiv 657148.
[78]	Song R, Sarnoski EA, Acar M (2018) The Systems Biology of Single-Cell Aging. iScience 7: 154-169. doi: 10.1016/j.isci.2018.08.023
[79]	Coffman JA, Rieger S, Rogers AN, et al. (2016) Comparative biology of tissue repair, regeneration and aging. npj Regen Med 1: 16003. doi: 10.1038/npjregenmed.2016.3
[80]	Hasin Y, Seldin M, Lusis A (2017) Multi-omics approaches to disease. Genome Biol 18: 83. doi: 10.1186/s13059-017-1215-1
[81]	Safian MF, Zinn N, Seidler J, et al. (2016) Microquantification of phospholipid classes by stable isotope dilution and nanoESI mass spectrometry. Anal Bioanal Chem 408: 7663-7667. doi: 10.1007/s00216-016-9859-3
[82]	Simmons AJ, Scurrah CR, McKinley ET, et al. (2016) Impaired coordination between signaling pathways is revealed in human colorectal cancer using single-cell mass cytometry of archival tissue blocks. Sci Signal 9: rs11. doi: 10.1126/scisignal.aah4413
[83]	Ginsberg SD, Che S, Counts SE, et al. (2006) Single cell gene expression profiling in Alzheimer's disease. NeuroRx 3: 302-318. doi: 10.1016/j.nurx.2006.05.007
[84]	Elstner M, Morris CM, Heim K, et al. (2009) Single-cell expression profiling of dopaminergic neurons combined with association analysis identifies pyridoxal kinase as Parkinson's disease gene. Ann Neurol 66: 792-798. doi: 10.1002/ana.21780
[85]	Darmanis S, Sloan SA, Zhang Y, et al. (2015) A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci 112: 7285-7290. doi: 10.1073/pnas.1507125112
[86]	Yamada A, Renault R, Chikina A, et al. (2016) Transient microfluidic compartmentalization using actionable microfilaments for biochemical assays, cell culture and organs-on-chip. Lab Chip 16: 4691-4701. doi: 10.1039/C6LC01143H
[87]	Srikakulapu P, Hu D, Yin C, et al. (2016) Artery Tertiary Lymphoid Organs Control Multilayered Territorialized Atherosclerosis B-Cell Responses in Aged ApoE-/- Mice. Arterioscler Thromb Vasc Biol 36: 1174-1185. doi: 10.1161/ATVBAHA.115.306983
[88]	Winkels H, Ehinger E, Vassallo M, et al. (2018) Atlas of the Immune Cell Repertoire in Mouse Atherosclerosis Defined by Single-Cell RNA-Sequencing and Mass Cytometry. Circ Res 122: 1675-1688. doi: 10.1161/CIRCRESAHA.117.312513
[89]	Gladka MM, Molenaar B, de Ruiter H, et al. (2018) Single-Cell Sequencing of the Healthy and Diseased Heart Reveals Cytoskeleton-Associated Protein 4 as a New Modulator of Fibroblasts Activation. Circulation 138: 166-180. doi: 10.1161/CIRCULATIONAHA.117.030742
[90]	Jia G, Preussner J, Chen X, et al. (2018) Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement. Nat Commun 9: 4877. doi: 10.1038/s41467-018-07307-6
[91]	Ashton MP, Eugster A, Dietz S, et al. (2019) Association of Dendritic Cell Signatures With Autoimmune Inflammation Revealed by Single-Cell Profiling. Arthritis Rheumatol 71: 817-828. doi: 10.1002/art.40793
[92]	Wollny D, Zhao S, Everlien I, et al. (2016) Single-Cell Analysis Uncovers Clonal Acinar Cell Heterogeneity in the Adult Pancreas. Dev Cell 39: 289-301. doi: 10.1016/j.devcel.2016.10.002
[93]	Kallionpää H, Somani J, Tuomela S, et al. (2019) Early Detection of Peripheral Blood Cell Signature in Children Developing β-Cell Autoimmunity at a Young Age. Diabetes 68: 2024-2034. doi: 10.2337/db19-0287
[94]	Jin Z, Fan W, Jensen MA, et al. (2017) Single-cell gene expression patterns in lupus monocytes independently indicate disease activity, interferon and therapy. Lupus Sci Med 4: e000202. doi: 10.1136/lupus-2016-000202
[95]	O'Gorman WE, Kong DS, Balboni IM, et al. (2017) Mass cytometry identifies a distinct monocyte cytokine signature shared by clinically heterogeneous pediatric SLE patients. J Autoimmun S0896-8411: 30412-7.
[96]	Artis D, Spits H (2015) The biology of innate lymphoid cells. Nature 517: 293-301. doi: 10.1038/nature14189
[97]	Bedard PL, Hansen AR, Ratain MJ, et al. (2013) Tumour heterogeneity in the clinic. Nature 501: 355-364. doi: 10.1038/nature12627
[98]	Chung W, Eum HH, Lee HO, et al. (2017) Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat Commun 8: 15081. doi: 10.1038/ncomms15081
[99]	Xin Y, Kim J, Ni M, et al. (2016) Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. Proc Natl Acad Sci U S A 113: 3293-3298. doi: 10.1073/pnas.1602306113
[100]	Gong H, Do D, Ramakrishnan R (2018) Single-Cell mRNA-Seq Using the Fluidigm C1 System and Integrated Fluidics Circuits. Methods Mol Biol 1783: 193-207. doi: 10.1007/978-1-4939-7834-2_10
[101]	DeLaughter DM (2018) The Use of the Fluidigm C1 for RNA Expression Analyses of Single Cells. Curr Protoc Mol Biol 122: e55. doi: 10.1002/cpmb.55
[102]	Capper D (2012) Addressing Diffuse Glioma as a Systemic Brain Disease With Single-Cell Analysis. Arch Neurol 69: 523. doi: 10.1001/archneurol.2011.2910
[103]	Wang Y, Waters J, Leung ML, et al. (2014) Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512: 155-160. doi: 10.1038/nature13600
[104]	Gorgannezhad L, Umer M, Islam MN, et al. (2018) Circulating tumor DNA and liquid biopsy: opportunities, challenges, and recent advances in detection technologies. Lab Chip 18: 1174-1196. doi: 10.1039/C8LC00100F
[105]	Burinaru TA, Avram M, Avram A, et al. (2018) Detection of Circulating Tumor Cells Using Microfluidics. ACS Comb Sci 20: 107-126. doi: 10.1021/acscombsci.7b00146
[106]	Islam M, Asghar W, Kim Y (2014) Cell Elasticity-based Microfluidic Label-free Isolation of Metastatic Tumor Cells. J Adv Med Med Res 4: 2129-2140. doi: 10.9734/BJMMR/2014/7392
[107]	Chen W, Weng S, Zhang F, et al. (2013) Nanoroughened Surfaces for E ffi cient Capture of Circulating Tumor Cells without Using Capture Antibodies. ACS Nano 7: 566-575. doi: 10.1021/nn304719q
[108]	Islam M, Hasan MR, Sajid A, et al. (2016) Electrical Profiling and Aptamer Functionalized Nanotextured Surface in a Single Biochip for the Detection of Tumor Cells. Funct Nanostruct 13-21.
[109]	Shen Y, Nakajima M, Kojima S, et al. (2011) Single cell adhesion force measurement for cell viability identification using an AFM cantilever-based micro putter. Meas Sci Technol 22: 115802. doi: 10.1088/0957-0233/22/11/115802
[110]	Huang S, Ingber DE (1999) The structural and mechanical complexity of cell-growth control. Nat Cell Biol 1: E131-138. doi: 10.1038/13043
[111]	Lasky LA, Singer MS, Dowbenko D, et al. (1992) An endothelial ligand for L-Selectin is a novel mucin-like molecule. Cell 69: 927-938. doi: 10.1016/0092-8674(92)90612-G
[112]	Szekanecz Z, Koch AE (2000) Cell-cell interactions in synovitis. Endothelial cells and immune cell migration. Arthritis Res 2: 368-373. doi: 10.1186/ar114
[113]	Okegawa T, Pong RC, Li Y, et al. (2004) The role of cell adhesion molecule in cancer progression and its application in cancer therapy. Acta Biochim Pol 51: 445-457. doi: 10.18388/abp.2004_3583
[114]	Hirohashi S, Kanai Y (2003) Cell adhesion system and human cancer morphogenesis. Cancer Sci 94: 575-581. doi: 10.1111/j.1349-7006.2003.tb01485.x
[115]	Perinpanayagam H, Zaharias R, Stanford C, et al. (2001) Early cell adhesion events differ between osteoporotic and non-osteoporotic osteoblasts. J Orthop Res 19: 993-1000. doi: 10.1016/S0736-0266(01)00045-6
[116]	Serhan CN, Savill J (2005) Resolution of inflammation: the beginning programs the end. Nat Immunol 6: 1191-1197. doi: 10.1038/ni1276
[117]	Simon SI, Green CE (2005) Molecular Mechanics and Dynamics of Leukocyte Recruitment During Inflammation. Annu Rev Biomed Eng 7: 151-185. doi: 10.1146/annurev.bioeng.7.060804.100423
[118]	Oh KS, Patel H, Gottschalk RA, et al. (2017) Anti-Inflammatory Chromatinscape Suggests Alternative Mechanisms of Glucocorticoid Receptor Action. Immunity 47: 298-309.e5. doi: 10.1016/j.immuni.2017.07.012
[119]	Frisch SM, Francis H (1994) Disruption of epithelial cell-matrix interaction induces apoptosis. J Cell Biol 124: 619-626. doi: 10.1083/jcb.124.4.619
[120]	Simpson CD, Anyiwe K, Schimmer AD (2008) Anoikis resistance and tumor metastasis. Cancer Lett 272: 177-185. doi: 10.1016/j.canlet.2008.05.029
[121]	Trott DW, Henson GD, Ho MHT, et al. (2018) Age-related arterial immune cell infiltration in mice is attenuated by caloric restriction or voluntary exercise. Exp Gerontol 109: 99-107. doi: 10.1016/j.exger.2016.12.016
[122]	Valencia AMJ, Wu PH, Yogurtcu ON, et al. (2015) Collective cancer cell invasion induced by coordinated contractile stresses. Oncotarget 6: 43438-43451. doi: 10.18632/oncotarget.5874
[123]	Helenius J, Heisenberg CP, Gaub HE, et al. (2008) Single-cell force spectroscopy. J Cell Sci 121: 1785-1791. doi: 10.1242/jcs.030999
[124]	Mao S, Zhang Q, Li H, et al. (2018) Measurement of Cell–Matrix Adhesion at Single-Cell Resolution for Revealing the Functions of Biomaterials for Adherent Cell Culture. Anal Chem 90: 9637-9643. doi: 10.1021/acs.analchem.8b02653
[125]	Kwon KW, Choi SS, Lee SH, et al. (2007) Label-free, microfluidic separation and enrichment of human breast cancer cells by adhesion difference. Lab Chip 7: 1461-1468. doi: 10.1039/b710054j
[126]	de Wit J, Ghosh A (2015) Specification of synaptic connectivity by cell surface interactions. Nat Rev Neurosci 17: 4. doi: 10.1038/nrn.2015.3
[127]	Speicher MR (2013) Single-cell analysis: toward the clinic. Genome Med 5: 74. doi: 10.1186/gm478
[128]	Xie Y, Nama N, Li P, et al. (2016) Probing Cell Deformability via Acoustically Actuated Bubbles. Small 12: 902-910. doi: 10.1002/smll.201502220
[129]	Shaffer SM, Dunagin MC, Torborg SR, et al. (2017) Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546: 431-435. doi: 10.1038/nature22794
[130]	Yuan GC, Cai L, Elowitz M, et al. (2017) Challenges and emerging directions in single-cell analysis. Genome Biol 18: 84. doi: 10.1186/s13059-017-1218-y
[131]	Hayes J, Thygesen H, Tumilson C, et al. (2015) Prediction of clinical outcome in glioblastoma using a biologically relevant nine-microRNA signature. Mol Oncol 9: 704-714. doi: 10.1016/j.molonc.2014.11.004
[132]	Goldstein LD, Chen YJJ, Dunne J, et al. (2017) Massively parallel nanowell-based single-cell gene expression profiling. BMC Genomics 18: 519. doi: 10.1186/s12864-017-3893-1
[133]	Aytes A, Mitrofanova A, Lefebvre C, et al. (2014) Cross-Species Regulatory Network Analysis Identifies a Synergistic Interaction between FOXM1 and CENPF that Drives Prostate Cancer Malignancy. Cancer Cell 25: 638-651. doi: 10.1016/j.ccr.2014.03.017
[134]	Peyer KE, Zhang L, Nelson BJ (2013) Bio-inspired magnetic swimming microrobots for biomedical applications. Nanoscale 5: 1259-1272. doi: 10.1039/C2NR32554C
[135]	Yamanaka YJ, Szeto GL, Gierahn TM, et al. (2012) Cellular Barcodes for Efficiently Profiling Single-Cell Secretory Responses by Microengraving. Anal Chem 84: 10531-10536. doi: 10.1021/ac302264q
[136]	Song R, Acar M (2019) Stochastic modeling of aging cells reveals how damage accumulation, repair, and cell-division asymmetry affect clonal senescence and population fitness. BMC Bioinformatics 20: 391. doi: 10.1186/s12859-019-2921-3
[137]	Bressloff PC, Newby JM (2013) Stochastic models of intracellular transport. Rev Mod Phys 85: 135-196. doi: 10.1103/RevModPhys.85.135
[138]	Ribeiro RDC, Pal D, Jamieson D, et al. (2017) Temporary Single-Cell Coating for Bioprocessing with a Cationic Polymer. ACS Appl Mater Interfaces 9: 12967-12974. doi: 10.1021/acsami.6b16434
[139]	Kharchenko PV, Silberstein L, Scadden DT (2014) Bayesian approach to single-cell differential expression analysis. Nat Methods 11: 740-742. doi: 10.1038/nmeth.2967

Reader Comments

Your name:*

Email:*
© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)