Kemajuan dalam Biologi Spasial: Perspektif Multiomik

Raymond R. Tjandrawinata

doi:10.56951/54mmsr80

Kemajuan dalam Biologi Spasial: Perspektif Multiomik

Penulis

Raymond R. Tjandrawinata Dexa Laboratories of Biomolecular Sciences, Dexa Group, Jawa Barat, Indonesia/Center for Pharmaceutical and Nutraceutical Research and Policy, Unika Atma Jaya, Jakarta, Indonesia

DOI:

https://doi.org/10.56951/54mmsr80

Kata Kunci:

biologi spasial, imunofluoresensi multipleks, kecerdasan buatan, mikrobioma, multiomik, transkriptomik spasial

Abstrak

Biologi spasial telah muncul sebagai bidang transformatif, memberikan wawasan yang belum pernah terjadi sebelumnya tentang organisasi spasial biomolekul, sel, dan jaringan. Ulasan ini mengkaji kemajuan dalam multiomik spasial antara tahun 2020 hingga 2025, dengan fokus pada teknologi utama, termasuk imunofluoresensi multipleks (multiplex immunofluorescence/mIF), mikroskopi lembaran cahaya, dan transkriptomik spasial. Inovasi-inovasi ini memungkinkan analisis multidimensi resolusi tinggi terhadap sistem biologis yang kompleks, merevolusi penelitian kanker, studi mikrobioma, dan ilmu saraf. Integrasi antara omik sel tunggal dan spasial telah memberikan pemahaman yang lebih dalam mengenai heterogenitas tumor, distribusi sel imun, dan interaksi seluler dalam lingkungan mikro tumor. Selain itu, multiomik spasial telah memperluas aplikasi di luar onkologi, menawarkan wawasan baru dalam biologi liver, sirkuit saraf, dan mikrobioma usus. Namun demikian, dengan adanya kemajuan ini, tetap terdapat tantangan dalam analisis data, integrasi, dan aksesibilitas. Evolusi berkelanjutan alat komputasi, analisis berbasis kecerdasan buatan, dan integrasi multiomik akan semakin meningkatkan dampak bidang ini, serta membuka jalan bagi pengobatan yang lebih personal dan terapi presisi.

Unduhan

Baca Selengkapnya

Referensi

1. Asp M, Bergenstrhle J, Lundeberg J. Spatially resolved transcriptomes next generation tools for tissue exploration. Bioessays 2020;42(10):e1900221. doi: 10.1002/bies.201900221.

2. Perillo B, Donato MD, Pezone A, Zazzo ED, Giovannelli P, Galasso G, et al. Ros in cancer therapy: the bright side of the moon. Experimental and Molecular Medicine. 2020;52(2):192–203. doi: 10.1038/s12276-020-0384-2.

3. Rozenblatt-Rosen O, Regev A, Oberdoerffer P, Nawy T, Hupalowska A, Rood J, et al. The human tumor atlas network: charting tumor transitions across space and time at single-cell resolution. Cell. 2020;181(2):236–49. doi: 10.1016/j.cell.2020.03.053.

4. Liao J, Lu X, Shao X, Zhu L, Fan X. Uncovering an organs molecular architecture at single-cell resolutio

by spatially resolved transcriptomics. Trends in Biotechnology. 2021;39(1):43–58. doi: 10.1016/j.tibtech.2020.05.006.

5. Wu K, Lin K, Li X, Yuan X, Xu P, Ni P, et al. Redefining tumor-associated macrophage subpopulations and functions in the tumor microenvironment. Frontiers in Immunology. 2020;11:1731. doi: 10.3389/fimmu.2020.01731.

6. Parsons J and Francavilla C. Omics approaches to explore the breast cancer landscape. Frontiers in Cell and Developmental Biology. 2020;7:395. doi: 10.3389/fcell.2019.00395.

7. Ma R, Black A, Qian B. Macrophage diversity in cancer revisited in the era of single-cell omics. Trends in Immunology. 2022;43(7):546–63. doi: 10.1016/j.it.2022.04.008.

8. Tsuruta S, Kawasaki T, Machida M, Iwatsuki K. Inaba A, Shibata S, et al. Development of human gut organoids with resident tissue macrophages as a model of intestinal immune responses. Cell Mol Gastroenterol Hepatol. 2022;14(3):726–9.e5. doi: 10.1016/j.jcmgh.2022.06.006.

9. Wang X, Almet AA, Nie Q. The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics. Semin Cancer Biol. 2023;95:42–51. doi: 10.1016/j.semcancer.2023.07.001.

10. Park H, Jo S, Lee RH, Macks C, Ku T, Park J, et al. Spatial transcriptomics: technical aspects of recent developments and their applications in neuroscience and cancer research. Advanced Science 2023;10(16): 2206939. doi: 10.1002/advs.202206939.

11. Chen C, Wang J, Pan D, Wang X, Xu Y, Yan J, et al. Applications of multiomics analysis in human diseases. MedComm (2020). 2023;4(4):e315. doi: 10.1002/mco2.315.

12. Yu Q, Jiang M, Wu L. Spatial transcriptomics technology in cancer research. Front Oncol. 2022;12:1019111. doi: 10.3389/fonc.2022.1019111.

13. Taube JM, Aktrk G, Angelo M, Engle EL, Gnjatic S, Greenbaum S, et al. The society for immunotherapy of cancer statement on best practices for multiplex immunohistochemistry (ihc) and immunofluorescence (if) staining and validation. J Immunother Cancer. 2020;8(1):e000155. doi: 10.1136/jitc-2019-000155.

14. Schulz D, Renaud-Tissot S, Liechti R, Eling N, Daniel M, Windhager J, et al. Abstract 2756: a toolbox enabling a data-driven selection of regions of interest in tumor tissue sections for imaging mass cytometry analysis. Cancer Research 2021;81(13 Suppl.). doi: 10.1158/1538-7445.AM2021-2756.

15. Ly CP, Veletic I, Pacheco CD, Dasdemir E, Jelloul F, Ferri-Borgogno S, et al. Multimodal spatial profiling in acute myeloid leukemia. Blood 2024;144(Supplement 1):2681. doi: 10.1182/blood-2024-203816.

16. Ge Q, Zhao Z, Li X, Yang F, Zhang M, Hao Z, et al. Deciphering the suppressive immune microenvironment of prostate cancer based on cd4+ regulatory t cells: implications for prognosis and therapy prediction. Clin Transl Med. 2024;14(1):e1552. doi: 10.1002/ctm2.1552.

17. Diao X, Guo C, Li S. Pd1/pdl1 colocation: a novel biomarker for immunotherapy response in nonsmall cell lung cancer. Thorac Cancer 2022;13(11):1545–7. doi: 10.1111/1759-7714.14436.

18. Vu T, Vallmitjana A, Gu J, La K, Xu Q, Flores J, et al. Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis. Nat Commun. 2022;13(1):169. doi: 10.1038/s41467-021-27798-0.

19. Maibach F, Sadozai H, Jafari SMS, Hunger RE, Schenk M. Tumor-infiltrating lymphocytes and their prognostic value in cutaneous melanoma. Front Immunol. 2020;11:2105. doi: 10.3389/fimmu.2020.02105.

20. Fu T, Dai L, Wu S, Xiao Y, Ma D, Jiang Y, et al. Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response. J Hematol Oncol. 2021;14(1):98. doi: 10.1186/s13045-021-01103-4.

21. Boisson A, Noel GR, Saiselet M, Rodrigues-Vitria J, Thomas N, Fontsa ML, et al. Fluorescent multiplex immunohistochemistry coupled with other state-of-the-art techniques to systematically characterize the tumor immune microenvironment. Front Mol Biosci. 2021:8:673042. doi: 10.3389/fmolb.2021.673042.

22. Gao TT, Oh T, Mehta K, Huang YA, Camp T, Fan H, et al. The clinical potential of optogenetic interrogation of pathogenesis. Clinical and Translational Medicine 2023;13(5):e1243. doi: 10.1002/ctm2.1243.

23. Erfanian N, Heydari AA, Picazo PI, Derakhshani A, GhasemiGol M, Farahpour M, et al. Deep learning applications in single-cell omics data analysis. Biomed Pharmacother. 2023;165:115077. doi: 10.1016/j.biopha.2023.115077.

24. Macklin A, Khan S, Kislinger T. Recent advances in mass spectrometry based clinical proteomics: applications to cancer research. Clin Proteomics 2020;17:17. doi: 10.1186/s12014-020-09283-w.

25. Wang Y, Liu B, Zhao G, Lee Y, Buzdin A, Mu X, et al. Spatial transcriptomics: technologies, applications and experimental considerations. Genomics 2023;115(5):110671. doi: 10.1016/j.ygeno.2023.110671.

26. Jiang Y, Hao S, Chen X, Cheng M, Xu J, Li C, et al. Spatial transcriptome uncovers the mouse lung architectures and functions. Front. Genet. 13:858808. doi: 10.3389/fgene.2022.858808.

27. Yang H, Zuo Y, Li G, Liu W, Pan Y, Fan T, et al. Advances in spatial transcriptomics and its applications in cancer research. Mol Cancer 2024;23 https://doi.org/10.1186/s12943-024-02040-9.

28. Duan H, Cheng T, Cheng H. Spatially resolved transcriptomics: advances and applications. Blood Sci. 2022;5(1):1–14. doi: 10.1097/BS9.0000000000000141.

29. Troul’e K, Petryszak R, Prete M, Cranley J, Harasty A, Tuong Z, et al. Cellphonedb v5: inferring cell-cell communication from single-cell multiomics data. arXiv preprint arXiv 2023:2311.04567.

30. Marei HE, Hasan A, Pozzoli G, Cenciarelli C. Cancer immunotherapy with immune checkpoint inhibitors (icis): potential, mechanisms of resistance, and strategies for reinvigorating t cell responsiveness when resistance is acquired. Cancer Cell Int. 2023;23(1):64. doi: 10.1186/s12935-023-02902-0.

31. Almagro J, Messal HA, Elsegui-Artola A, Rheenen JV, Behrens A. Tissue architecture in tumor initiation and progression. Trends Cancer. 2022;8(6):494–505. doi: 10.1016/j.trecan.2022.02.007.

32. Paris J and Henderson NC. Liver zonation, revisited. Hepatology 2022;76(4):1219–30. doi: 10.1002/hep.32408.

33. Lin D, Shen L, Luo M, Zhang K, Li J, Yang Q, et al. Circulating tumor cells: biology and clinical significance. Sig Transduct Target Ther 2021;6:404. doi: 10.1038/s41392-021-00817-8.

34. Huang P, Gao W, Fu C, Tian R. Functional and clinical proteomic exploration of pancreatic cancer. Mol Cell Proteomics 2023;22(7):100575. doi: 10.1016/j.mcpro.2023.100575.

35. Feng D, Zhu W, Wang J, Li D, Shi X, Xiong Q, et al. The implications of single-cell rna-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy. Mil Med Res. 2024;11(1):21. doi: 10.1186/s40779-024-00526-7.

36. Hadrich D. New eu projects delivering human microbiome applications. Future Science Ltd. https://doi.org/10.2144/fsoa-2020-0028.

37. Klionsky DJ, Petroni G, Amaravadi RK, Baehrecke EH, Ballabio A, Boya P, et al. Autophagy in major human diseases. EMBO J. 2021;40(19):e108863. doi: 10.15252/embj.2021108863.

38. He J, Deng C, Krall L, Shan Z. Scrna-seq and st-seq in liver research. Cell Regen 2023;12:11. doi: 10.1186/s13619-022-00152-5.

39. Waqas A, Tripathi A, Ramachandran RP, Stewart PA, Rasool G. Multimodal data integration for oncology in the era of deep neural networks: a review. Front Artif Intell. 2024:7:1408843. doi: 10.3389/frai.2024.1408843.

40. Erfanian N, Heydari AA, Feriz AM, Picazo PI, Derakshani A, GhasemiGol M, et al. Deep learning applications in single-cell genomics and transcriptomics data analysis. Biomed Pharmacother 2023:165:115077. doi:10.1016/j.biopha.2023.115077.

41. Jeon H, Xie J, Jeon Y, Jung KJ, Gupta A, Chang W, et al. Statistical power analysis for designing bulk, single-cell, and spatial transcriptomics experiments: review, tutorial, and perspectives. Biomolecules 2023;13(2):221. doi: 10.3390/biom13020221.

42. Polychronidou M, Hou J, Babu MM, Liberali P, Amit I, Deplancke B, et al. Single cell biology: what does the future hold? Mol Syst Biol. 2023;19(7):e11799. doi: 10.15252/msb.202311799.

43. Omenn GS, Lane L, Overall CM, Pineau C, Packer NH, Cristea IM, et al. The 2022 report on the human proteome from the hupo human proteome project. J Proteome Res. 2023;22(4):1024–42. doi: 10.1021/acs.jproteome.2c00498.

44. Birhanu AG. Mass spectrometry-based proteomics as an emerging tool in clinical laboratories. Clin Proteomics 2023;20(1):32. doi: 10.1186/s12014-023-09424-x.

45. Bahar ME, Kim HJ, Kim DR. Targeting the ras/raf/mapk pathway for cancer therapy: from mechanism to clinical studies. Signal Transduct Target Ther. 2023;8(1):455. doi: 10.1038/s41392-023-01705-z.

46. Lhnemann D, Kster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, et al. Eleven grand challenges in single-cell data science. Genome Biol. 2020;21(1):31. doi: 10.1186/s13059-020-1926-6.