Version 1
: Received: 30 October 2024 / Approved: 31 October 2024 / Online: 31 October 2024 (15:24:35 CET)
How to cite:
Ortega-de Martínez, A.; Jaén-Alvarado, Y.; Moreno-Labrador, D.; Gómez, N.; García, G.; Guerrero, E. N. Single Cell Sequencing: Genomic and Transcriptomics Approaches in Cancer Cell Biology. Preprints2024, 2024102601. https://doi.org/10.20944/preprints202410.2601.v1
Ortega-de Martínez, A.; Jaén-Alvarado, Y.; Moreno-Labrador, D.; Gómez, N.; García, G.; Guerrero, E. N. Single Cell Sequencing: Genomic and Transcriptomics Approaches in Cancer Cell Biology. Preprints 2024, 2024102601. https://doi.org/10.20944/preprints202410.2601.v1
Ortega-de Martínez, A.; Jaén-Alvarado, Y.; Moreno-Labrador, D.; Gómez, N.; García, G.; Guerrero, E. N. Single Cell Sequencing: Genomic and Transcriptomics Approaches in Cancer Cell Biology. Preprints2024, 2024102601. https://doi.org/10.20944/preprints202410.2601.v1
APA Style
Ortega-de Martínez, A., Jaén-Alvarado, Y., Moreno-Labrador, D., Gómez, N., García, G., & Guerrero, E. N. (2024). Single Cell Sequencing: Genomic and Transcriptomics Approaches in Cancer Cell Biology. Preprints. https://doi.org/10.20944/preprints202410.2601.v1
Chicago/Turabian Style
Ortega-de Martínez, A., Gabriela García and Erika N Guerrero. 2024 "Single Cell Sequencing: Genomic and Transcriptomics Approaches in Cancer Cell Biology" Preprints. https://doi.org/10.20944/preprints202410.2601.v1
Abstract
This article reviews the transformative impact of single-cell whole genome sequencing (scWGS) on cancer biology research. ScWGS has revolutionized our understanding of tumor heterogeneity, clonal evolution, and the tumor microenvironment by enabling high-resolution analysis of individual cells' genomic, transcriptomic, and epigenomic profiles. The technology's unique capabilities, including detection of rare genomic events, simultaneous capture of multiple genomic features, and association with phenotypic data, have opened new avenues for cancer research and precision medicine. Integration of scWGS with other single-cell omics technologies has provided a multidimensional view of cellular states and regulatory mechanisms in cancer. Advanced data analysis tools, including machine learning and AI algorithms, have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and development of predictive models for patient stratification. The article also discusses emerging technologies like spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor spatial organization and cellular interactions. As scWGS and related technologies continue to evolve, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
