Advancements in sequencing technologies:
from genomic revolution to single-cell insights in precision medicine
DOI:
https://doi.org/10.60087/jklst.v3.n4.p108Keywords:
Genetics, Single-cell sequencing, Next-generation sequencing, Cancer genomics, Precision medicineAbstract
Single-cell Sequencing (SCS) technologies, methods for analyzing genetic material at the single-cell level, offer extensive insights into cellular heterogeneity. This has broadened oncology research by enabling the exploration of functional and genetic diversity within tissues of different cell types. Furthermore, SCS facilitates the study of complex biological processes like metastasis tracking and tumor microenvironment analysis. However, the implementation of SCS methods is furrowed by a lack of clinical accessibility and high application costs. This review examines the development of SCS technologies, analyzing trends in throughput, accessibility, and cost of various commercial platforms, by focusing on the domain of cancer research and precision medicine. Despite the significant advancements offered by third-generation sequencing platforms, which provide high accuracy, versatility, and throughput for sequencing single-cell genetic information, these methods face challenges such as high error rates, insufficient funding, and complex data analysis. Furthermore, we’ve determined that the advancements of the previous decade have enabled personalized medicine and in-depth analysis of cellular heterogeneity, revolutionizing fields like medicine, biotechnology, and biological research. We anticipate our assay indicating extensive advancements in healthcare through the adoption of precision medicine concerning individual genomes and helping to demonstrate the promise of advancement in general understandings of complex biological systems. Furthermore, our research indicates that efforts to overcome technical, analytical, and cost-related challenges are essential in future clinical application, distribution, and growth of SCS methods.
Downloads
References
J. C. Venter et al., “The sequence of the human genome,” Science, vol. 291, no. 5507, pp. 1304–1351, Feb. 2001, doi: https://doi.org/10.1126/science.1058040.
A. Bird, “Perceptions of epigenetics,” Nature, vol. 447, no. 7143, pp. 396–398, May 2007, doi: https://doi.org/10.1038/nature05913.
E. Shapiro, T. Biezuner, and S. Linnarsson, “Single-cell sequencing-based technologies will revolutionize whole-organism science,” Nature Reviews. Genetics, vol. 14, no. 9, pp. 618–630, Jul. 2013, doi: https://doi.org/10.1038/nrg3542.
X. Ren, B. Kang, and Z. Zhang, “Understanding tumor ecosystems by single-cell sequencing: promises and limitations,” Genome Biology, vol. 19, no. 1, Dec. 2018, doi: https://doi.org/10.1186/s13059-018-1593-z.
E. Z. Macosko et al., “Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets,” Cell, vol. 161, no. 5, pp. 1202–1214, May 2015, doi: https://doi.org/10.1016/j.cell.2015.05.002.
N. Navin et al., “Tumour evolution inferred by single-cell sequencing,” Nature, vol. 472, no. 7341, pp. 90–94, Mar. 2011, doi: https://doi.org/10.1038/nature09807.
B. Lim, Y. Lin, and N. Navin, “Advancing Cancer Research and Medicine with Single-Cell Genomics,” Cancer Cell, vol. 37, no. 4, pp. 456–470, Apr. 2020, doi: https://doi.org/10.1016/j.ccell.2020.03.008.
J. D. Watson and F. H. C. Crick, “Molecular structure of nucleic acids: A structure for deoxyribose nucleic acid,” Nature, vol. 171, no. 4356, pp. 737–738, Apr. 1953, doi:https://doi.org/10.1038/171737a0.
F. Sanger, S. Nicklen, and A. R. Coulson, “DNA sequencing with chain-terminating inhibitors,” Proceedings of the National Academy of Sciences, vol. 74, no. 12, pp. 5463–5467, Dec. 1977, doi: https://doi.org/10.1073/pnas.74.12.5463.
E. S. Lander et al., “Initial sequencing and analysis of the human genome,” Nature, vol. 409, no. 6822, pp. 860–921, Feb. 2001, doi: https://doi.org/10.1038/35057062.
T. A. Manolio et al., “Implementing genomic medicine in the clinic: the future is here,” Genetics in Medicine, vol. 15, no. 4, pp. 258–267, Apr. 2013, doi: https://doi.org/10.1038/gim.2012.157.
X. Gan et al., “Multiple reference genomes and transcriptomes for Arabidopsis thaliana,” Nature, vol. 477, no. 7365, pp. 419–423, Sep. 2011, doi: https://doi.org/10.1038/nature10414.
D. R. Bentley et al., “Accurate whole human genome sequencing using reversible terminator chemistry,” Nature, vol. 456, no. 7218, pp. 53–59, Nov. 2008, doi: https://doi.org/10.1038/nature07517.
J. M. Rothberg et al., “An integrated semiconductor device enabling non-optical genome sequencing,” Nature, vol. 475, no. 7356, pp. 348–352, Jul. 2011, doi: https://doi.org/10.1038/nature10242.
J. Eid et al., “Real-Time DNA Sequencing from Single Polymerase Molecules,” Science, vol. 323, no. 5910, pp. 133–138, Jan. 2009, doi: https://doi.org/10.1126/science.1162986.
M. Jain, I. T. Fiddes, K. H. Miga, H. E. Olsen, B. Paten, and M. Akeson, “Improved data analysis for the MinION nanopore sequencer,” Nature Methods, vol. 12, no. 4, pp. 351–356, Apr. 2015, doi: https://doi.org/10.1038/nmeth.3290.
[17] M. Ruffalo, T. LaFramboise, and M. Koyuturk, “Comparative analysis of algorithms for next-generation sequencing read alignment,” Bioinformatics, vol. 27, no. 20, pp. 2790–2796, Aug. 2011, doi: https://doi.org/10.1093/bioinformatics/btr477.
[18] E. R. Mardis, “The Impact of Next-Generation Sequencing on Cancer Genomics: From Discovery to Clinic,” Cold Spring Harbor Perspectives in Medicine, vol. 9, no. 9, p. a036269, Nov. 2018, doi: https://doi.org/10.1101/cshperspect.a036269.
[19] M. De Zuani et al., “Single-cell and spatial transcriptomics analysis of non-small cell lung cancer,” Nature Communications, vol. 15, no. 1, p. 4388, May 2024, doi: https://doi.org/10.1038/s41467-024-48700-8.
F. Meacham, D. Boffelli, J. Dhahbi, D. I. Martin, M. Singer, and L. Pachter, “Identification and correction of systematic error in high-throughput sequence data,” BMC Bioinformatics, vol. 12, no. 1, Nov. 2011, doi: https://doi.org/10.1186/1471-2105-12-451.
S. El-Metwally, T. Hamza, M. Zakaria, and M. Helmy, “Next-Generation Sequence Assembly: Four stages of data processing and computational challenges,” PLOS Computational Biology/PLoS Computational Biology, vol. 9, no. 12, p. e1003345, Dec. 2013, doi: https://doi.org/10.1371/journal.pcbi.1003345.
S. Pei, T. Liu, X. Ren, W. Li, C. Chen, and Z. Xie, “Benchmarking variant callers in next-generation and third-generation sequencing analysis,” Briefings in Bioinformatics, vol. 22, no. 3, Jul. 2020, doi: https://doi.org/10.1093/bib/bbaa148.
L. A. Cohen-Aharonov et al., “High throughput SARS-CoV-2 variant analysis using molecular barcodes coupled with next generation sequencing,” PloS One, vol. 17, no. 6, p. e0253404, Jun. 2022, doi: https://doi.org/10.1371/journal.pone.0253404.
H.-Q. Qu, C. Kao, and H. Hakonarson, “Single-Cell RNA sequencing Technology landscape in 2023,” Stem Cells, vol. 42, no. 1, pp. 1–12, Nov. 2023, doi: https://doi.org/10.1093/stmcls/sxad077.
S. J. Clark, H. J. Lee, S. A. Smallwood, G. Kelsey, and W. Reik, “Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity,” Genome Biology, vol. 17, no. 1, Apr. 2016, doi: https://doi.org/10.1186/s13059-016-0944-x.
Z. Ji, W. Zhou, W. Hou, and H. Ji, “Single-cell ATAC-seq signal extraction and enhancement with SCATE,” Genome Biology, vol. 21, no. 1, Jul. 2020, doi: https://doi.org/10.1186/s13059-020-02075-3.
H. J. Pandya et al., “A microfluidic platform for drug screening in a 3D cancer microenvironment,” Biosensors and Bioelectronics, vol. 94, pp. 632–642, Aug. 2017, doi: https://doi.org/10.1016/j.bios.2017.03.054.
Gupte, P.; Dhingra, K.; Saloni , V. Precision Gene Editing Strategies With CRISPR-Cas9 for Advancing Cancer Immunotherapy and Alzheimer’s Disease. J. Knowl. Learn. Sci. Technol. 2024, 3 (4), 11-21. https://doi.org/10.60087/jklst.v3.n4.p11.
Kulkarni S, Dhingra K, Verma S., "Applications of CMUT Technology in Medical Diagnostics: From Photoa-coustic to Ultrasonic Imaging", International Journal of Science and Research (IJSR), Volume 13 Issue 6, June 2024, pp. 1264-1269, https://www.ijsr.net/ar-chive/v13i6/SR24619062609.pdf.
D. Lähnemann et al., “Eleven grand challenges in single-cell data science,” Genome Biology, vol. 21, no. 1, Feb. 2020, doi: https://doi.org/10.1186/s13059-020-1926-6.
C. Gawad, W. Koh, and S. R. Quake, “Single-cell genome sequencing: current state of the science,” Nature Reviews. Genetics, vol. 17, no. 3, pp. 175–188, Jan. 2016, doi: https://doi.org/10.1038/nrg.2015.16.
Pundlik, A.; Verma, S.; Dhingra, K. Neural Pathways Involved in Emotional Regulation and Emotional Intelligence. J. Knowl. Learn. Sci. Technol. 2024, 3 (3), 165-192. https://doi.org/10.60087/jklst.vol3.n3.p.165-192.
S. Yilmaz and A. K. Singh, “Single cell genome sequencing,” Current Opinion in Biotechnology, vol. 23, no. 3, pp. 437–443, Jun. 2012, doi: https://doi.org/10.1016/j.copbio.2011.11.018.
L. Valihrach, P. Androvic, and M. Kubista, “Platforms for Single-Cell collection and analysis,” International Journal of Molecular Sciences, vol. 19, no. 3, p. 807, Mar. 2018, doi: https://doi.org/10.3390/ijms19030807.
E. L. Van Dijk, H. Auger, Y. Jaszczyszyn, and C. Thermes, “Ten years of next-generation sequencing technology,” Trends in Genetics, vol. 30, no. 9, pp. 418–426, Sep. 2014, doi: https://doi.org/10.1016/j.tig.2014.07.001.
Y. Lei et al., “Applications of single-cell sequencing in cancer research: progress and perspectives,” Journal of Hematology & Oncology, vol. 14, no. 1, Jun. 2021, doi: https://doi.org/10.1186/s13045-021-01105-2.
S. Chen et al., “Single-cell analysis technologies for cancer research: from tumor-specific single cell discovery to cancer therapy,” Frontiers in Genetics, vol. 14, Oct. 2023, doi: https://doi.org/10.3389/fgene.2023.1276959.
K. Athanasopoulou, M. A. Boti, P. G. Adamopoulos, P. C. Skourou, and A. Scorilas, “Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics,” Life, vol. 12, no. 1, p. 30, Dec. 2021, doi: https://doi.org/10.3390/life12010030.
P. Deelen et al., “Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels,” Genome Medicine, vol. 7, no. 1, Mar. 2015, doi: https://doi.org/10.1186/s13073-015-0152-4.
H. J. Pandya et al., “Label-free electrical sensing of bacteria in eye wash samples: A step towards point-of-care detec-tion of pathogens in patients with infectious keratitis,” Biosensors and Bioelectronics, vol. 91, pp. 32–39, May 2017, doi: https://doi.org/10.1016/j.bios.2016.12.035.
D. Mancarella and C. Plass, “Epigenetic signatures in cancer: proper controls, current challenges and the potential for clinical translation,” Genome Medicine, vol. 13, no. 1, Feb. 2021, doi: https://doi.org/10.1186/s13073-021-00837-7.
S. Ambardar, R. Gupta, D. Trakroo, R. Lal, and J. Vakhlu, “High Throughput Sequencing: An Overview of sequencing chemistry,” Indian Journal of Microbiology/Indian Journal of Microbiology (Print), vol. 56, no. 4, pp. 394–404, Jul. 2016, doi: https://doi.org/10.1007/s12088-016-0606-4.
M. Kchouk, J.-F. Gibrat, and M. Elloumi, “Generations of Sequencing Technologies: From first to Next generation,” Biology and Medicine, vol. 09, no. 03, Jan. 2017, doi: https://doi.org/10.4172/0974-8369.1000395.
İ. Akaçin, Ş. Ersoy, O. Doluca, and M. Güngörmüşler, “Comparing the significance of the utilization of next generation and third generation sequencing technologies in microbial metagenomics,” Microbiological Research, vol. 264, p. 127154, Nov. 2022, doi: https://doi.org/10.1016/j.micres.2022.127154.
GhavamiNejad P, GhavamiNejad A, Zheng H, Dhingra K, Samarikhalaj M, Poudineh M., “A Conductive Hydrogel Mi-croneedle‐Based Assay Integrating PEDOT:PSS and Ag‐Pt Nanoparticles for Real‐Time, Enzyme‐Less, and Electro-chemical Sensing of Glucose,” Advanced Healthcare Materials, vol. 12, no. 1, Oct. 2022, doi: https://doi.org/10.1002/adhm.202202362.
M. Safavieh et al., “Paper microchip with a graphene-modified silver nano-composite electrode for electrical sensing of microbial pathogens,” Nanoscale, vol. 9, no. 5, pp. 1852–1861, 2017, doi: https://doi.org/10.1039/c6nr06417e.
J. L. Weirather et al., “Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis,” F1000Research, vol. 6, p. 100, Jun. 2017, doi: https://doi.org/10.12688/f1000research.10571.2.
S. Odinotski et al., “A Conductive Hydrogel‐Based Mi-croneedle Platform for Real‐Time pH Measurement in Live Animals,” Small, vol. 18, no. 45, Sep. 2022, doi: https://doi.org/10.1002/smll.202200201.
F. Wu et al., “Single-cell profiling of tumor heterogeneity and the microenvironment in advanced non-small cell lung cancer,” Nature Communications, vol. 12, no. 1, May 2021, doi: https://doi.org/10.1038/s41467-021-22801-0.
X. Ren, L. Zhang, Y. Zhang, Z. Li, N. Siemers, and Z. Zhang, “Insights Gained from Single-Cell Analysis of Immune Cells in the Tumor Microenvironment,” Annual Review of Immunology, vol. 39, no. 1, pp. 583–609, Apr. 2021, doi: https://doi.org/10.1146/annurev-immunol-110519-071134.
A. N. Hosein et al., “Cellular heterogeneity during mouse pancreatic ductal adenocarcinoma progression at single-cell resolution,” JCI Insight, vol. 4, no. 16, Aug. 2019, doi: https://doi.org/10.1172/jci.insight.129212.
Ruthwik Guntupalli, Saloni Verma and Karan Dhingra 2024. Impact of Healthcare Digitization: Systems Approach for Integrating Biosensor Devices and Electronic Health with Artificial Intelligence. American Scientific Research Journal for Engineering, Technology, and Sciences. 98, 1 (Aug. 2024), 246–257, https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/10786/2789.
J. Peng et al., “Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma,” Cell Research/Cell Research, vol. 29, no. 9, pp. 725–738, Jul. 2019, doi: https://doi.org/10.1038/s41422-019-0195-y.
Y. Han et al., “Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis,” Journal of Hematology & Oncology, vol. 15, no. 1, May 2022, doi: https://doi.org/10.1186/s13045-022-01280-w
Precision Medicine With Data-driven Approaches: A Framework For Clinical Translation - Simranjit Kaur, Rowan Kim, Nisha Javagal, Joseph Calderon, Senia Rodriguez, Nithin Murugan, Kelsang Gyatsho Bhutia, Karan Dhingra, Saloni Verma - AIJMR Volume 2, Issue 5, September-October 2024. https://www.aijmr.com/research-paper.php?id=1077.
R. T. Davis et al., “Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing,” Nature Cell Biology, vol. 22, no. 3, pp. 310–320, Mar. 2020, doi: https://doi.org/10.1038/s41556-020-0477-0.
J. Wang and Y. Song, “Single cell sequencing: a distinct new field,” Clinical and Translational Medicine, vol. 6, no. 1, Feb. 2017, doi: https://doi.org/10.1186/s40169-017-0139-4.
M. Fishman et al., “Twelve‐Month results from multicenter, open‐label, randomized controlled clinical trial comparing differential target multiplexed spinal cord stimulation and traditional spinal cord stimulation in subjects with chronic intractable back pain and leg pain,” Pain Practice, vol. 21, no. 8, pp. 912–923, Aug. 2021, doi: https://doi.org/10.1111/papr.13066.
B. Abell et al., “Identifying barriers and facilitators to successful implementation of computerized clinical decision support systems in hospitals: a NASSS framework-informed scoping review,” Implementation Science, vol. 18, no. 1, Jul. 2023, doi: https://doi.org/10.1186/s13012-023-01287-y.
Z. Fan, X. Shi, S. Yang, Y. Sun, and R. Chen, “Reliability and validity evaluation of the stigma of loneliness scale in Chinese college students,” BMC Public Health, vol. 24, no. 1, Jan. 2024, doi: https://doi.org/10.1186/s12889-024-17738-0.
C. M. Dorey, H. Baumann, and N. Biller-Andorno, “Patient data and patient rights: Swiss healthcare stakeholders’ ethical awareness regarding large patient data sets – a qualitative study,” BMC Medical Ethics, vol. 19, no. 1, Mar. 2018, doi: https://doi.org/10.1186/s12910-018-0261-x.
E. Jia et al., “Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues,” BMC Genomics, vol. 22, no. 1, Nov. 2021, doi: https://doi.org/10.1186/s12864-021-08132-w.
Y. Yu et al., “Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method,” Genome Biology, vol. 24, no. 1, Sep. 2023, doi: https://doi.org/10.1186/s13059-023-03047-z.
Y. Zhang, D. Tran, T. Nguyen, S. M. Dascalu, and F. C. Harris, “A robust and accurate single-cell data trajectory inference method using ensemble pseudotime,” BMC Bioinformatics, vol. 24, no. 1, Feb. 2023, doi: https://doi.org/10.1186/s12859-023-05179-2.
C. Wieder et al., “PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration,” PLOS Computational Biology/PLoS Computational Biology, vol. 20, no. 3, p. e1011814, Mar. 2024, doi: https://doi.org/10.1371/journal.pcbi.1011814.
M. Gužvić, “The History of DNA Sequencing / ISTORIJAT SEKVENCIRANJA DNK,” Journal of Medical Biochemistry, vol. 32, no. 4, pp. 301–312, Oct. 2013, doi: https://doi.org/10.2478/jomb-2014-0004.
H. Satam et al., “Next-Generation Sequencing Technology: Current trends and advancements,” Biology, vol. 12, no. 7, p. 997, Jul. 2023, doi: https://doi.org/10.3390/biology12070997.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2024 All rights reserved by the respective authors and JKLST.
Copyright: © The Author(s), 2024. Published by 