1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Pharmacology and Toxicology
  4. Volume 64, 2024
  5. Article

Annual Review of Pharmacology and Toxicology

Volume 64, 2024

Deciphering Drug Targets and Actions with Single-Cell and Spatial Resolution

Abstract

Recent advances in chemical, molecular, and genetic approaches have provided us with an unprecedented capacity to identify drug-target interactions across the whole proteome and genome. Meanwhile, rapid developments of single-cell and spatial omics technologies are revolutionizing our understanding of the molecular architecture of biological systems. However, a significant gap remains in how we align our understanding of drug actions, traditionally based on molecular affinities, with the in vivo cellular and spatial tissue heterogeneity revealed by these newer techniques. Here, we review state-of-the-art methods for profiling drug-target interactions and emerging multiomics tools to delineate the tissue heterogeneity at single-cell resolution. Highlighting the recent technical advances enabling high-resolution, multiplexable in situ small-molecule drug imaging (clearing-assisted tissue click chemistry, or CATCH), we foresee the integration of single-cell and spatial omics platforms, data, and concepts into the future framework of defining and understanding in vivo drug-target interactions and mechanisms of actions.

Keyword(s): CATCH, chemoproteomics, drug-target interactions, mechanism of action, single cell, spatial omics
Loading

Article metrics loading...

/content/journals/10.1146/annurev-pharmtox-033123-123610
2024-01-23
2024-04-28
Loading full text...

Full text loading...

/deliver/fulltext/pharmtox/64/1/annurev-pharmtox-033123-123610.html?itemId=/content/journals/10.1146/annurev-pharmtox-033123-123610&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E et al. 2019. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18:463–77
     [Google Scholar]
  2. 2.
    Finan C, Gaulton A, Kruger FA, Lumbers RT, Shah T et al. 2017. The druggable genome and support for target identification and validation in drug development. Sci. Transl. Med. 9:eaag1166
     [Google Scholar]
  3. 3.
    Davis RL. 2020. Mechanism of action and target identification: a matter of timing in drug discovery. iScience 23:101487
     [Google Scholar]
  4. 4.
    Mateus A, Kurzawa N, Perrin J, Bergamini G, Savitski MM. 2022. Drug target identification in tissues by thermal proteome profiling. Annu. Rev. Pharmacol. Toxicol. 62:465–82
     [Google Scholar]
  5. 5.
    Zeng H. 2022. What is a cell type and how to define it?. Cell 185:2739–55
     [Google Scholar]
  6. 6.
    Moses L, Pachter L. 2022. Museum of spatial transcriptomics. Nat. Methods 19:534–46
     [Google Scholar]
  7. 7.
    Elmentaite R, Dominguez Conde C, Yang L, Teichmann SA 2022. Single-cell atlases: shared and tissue-specific cell types across human organs. Nat. Rev. Genet. 23:395–410
     [Google Scholar]
  8. 8.
    Stuart T, Satija R. 2019. Integrative single-cell analysis. Nat. Rev. Genet. 20:257–72
     [Google Scholar]
  9. 9.
    Srivatsan SR, McFaline-Figueroa JL, Ramani V, Saunders L, Cao J et al. 2020. Massively multiplex chemical transcriptomics at single-cell resolution. Science 367:45–51
     [Google Scholar]
  10. 10.
    Wu Z, Lawrence PJ, Ma A, Zhu J, Xu D, Ma Q. 2020. Single-cell techniques and deep learning in predicting drug response. Trends Pharmacol. Sci. 41:1050–65
     [Google Scholar]
  11. 11.
    Vinegoni C, Dubach JM, Thurber GM, Miller MA, Mazitschek R, Weissleder R. 2015. Advances in measuring single-cell pharmacology in vivo. Drug. Discov. Today 20:1087–92
     [Google Scholar]
  12. 12.
    Zheng W, Li G, Li X. 2015. Affinity purification in target identification: the specificity challenge. Arch. Pharmacal Res. 38:1661–85
     [Google Scholar]
  13. 13.
    Kawatani M, Osada H. 2014. Affinity-based target identification for bioactive small molecules. MedChemComm 5:277–87
     [Google Scholar]
  14. 14.
    Meissner F, Geddes-McAlister J, Mann M, Bantscheff M. 2022. The emerging role of mass spectrometry-based proteomics in drug discovery. Nat. Rev. Drug. Discov. 21:637–54
     [Google Scholar]
  15. 15.
    Parker CG, Pratt MR. 2020. Click chemistry in proteomic investigations. Cell 180:605–32
     [Google Scholar]
  16. 16.
    Ha J, Park H, Park J, Park SB. 2021. Recent advances in identifying protein targets in drug discovery. Cell Chem. Biol. 28:394–423
     [Google Scholar]
  17. 17.
    Fellmann C, Gowen BG, Lin PC, Doudna JA, Corn JE. 2017. Cornerstones of CRISPR-Cas in drug discovery and therapy. Nat. Rev. Drug. Discov. 16:89–100
     [Google Scholar]
  18. 18.
    Vu V, Szewczyk MM, Nie DY, Arrowsmith CH, Barsyte-Lovejoy D. 2022. Validating small molecule chemical probes for biological discovery. Annu. Rev. Biochem. 91:61–87
     [Google Scholar]
  19. 19.
    Spradlin JN, Zhang E, Nomura DK. 2021. Reimagining druggability using chemoproteomic platforms. Acc. Chem. Res. 54:1801–13
     [Google Scholar]
  20. 20.
    Vincent F, Nueda A, Lee J, Schenone M, Prunotto M, Mercola M. 2022. Phenotypic drug discovery: recent successes, lessons learned and new directions. Nat. Rev. Drug. Discov. 21:899–914
     [Google Scholar]
  21. 21.
    Bantscheff M, Drewes G. 2012. Chemoproteomic approaches to drug target identification and drug profiling. Bioorg. Med. Chem. 20:1973–78
     [Google Scholar]
  22. 22.
    Niphakis MJ, Cravatt BF. 2014. Enzyme inhibitor discovery by activity-based protein profiling. Annu. Rev. Biochem. 83:341–77
     [Google Scholar]
  23. 23.
    Racioppo B, Qiu N, Adibekian A. 2023. Serine hydrolase activity-based probes for use in chemical proteomics. Isr. J. Chem. 63:e202300016
     [Google Scholar]
  24. 24.
    Cravatt BF. 2023. Activity-based protein profiling – finding general solutions to specific problems. Isr. J. Chem. 63:e202300029
     [Google Scholar]
  25. 25.
    Huang Z, Ogasawara D, Seneviratne UI, Cognetta AB 3rd, am Ende CW et al. 2019. Global portrait of protein targets of metabolites of the neurotoxic compound BIA 10-2474. ACS Chem. Biol. 14:192–97
     [Google Scholar]
  26. 26.
    van Esbroeck ACM, Janssen APA, Cognetta AB 3rd, Ogasawara D, Shpak G et al. 2017. Activity-based protein profiling reveals off-target proteins of the FAAH inhibitor BIA 10-2474. Science 356:1084–47
     [Google Scholar]
  27. 27.
    Bateman LA, Zaro BW, Miller SM, Pratt MR. 2013. An alkyne-aspirin chemical reporter for the detection of aspirin-dependent protein modification in living cells. J. Am. Chem. Soc. 135:14568–73
     [Google Scholar]
  28. 28.
    Lanning BR, Whitby LR, Dix MM, Douhan J, Gilbert AM et al. 2014. A road map to evaluate the proteome-wide selectivity of covalent kinase inhibitors. Nat. Chem. Biol. 10:760–67
     [Google Scholar]
  29. 29.
    Patricelli MP, Giang DK, Stamp LM, Burbaum JJ. 2001. Direct visualization of serine hydrolase activities in complex proteomes using fluorescent active site-directed probes. Proteomics 1:1067–71
     [Google Scholar]
  30. 30.
    Patricelli MP, Szardenings AK, Liyanage M, Nomanbhoy TK, Wu M et al. 2007. Functional interrogation of the kinome using nucleotide acyl phosphates. Biochemistry 46:350–58
     [Google Scholar]
  31. 31.
    Cravatt BF, Wright AT, Kozarich JW. 2008. Activity-based protein profiling: from enzyme chemistry to proteomic chemistry. Annu. Rev. Biochem. 77:383–414
     [Google Scholar]
  32. 32.
    Hahm HS, Toroitich EK, Borne AL, Brulet JW, Libby AH et al. 2020. Global targeting of functional tyrosines using sulfur-triazole exchange chemistry. Nat. Chem. Biol. 16:150–59
     [Google Scholar]
  33. 33.
    Ma N, Hu J, Zhang ZM, Liu W, Huang M et al. 2020. 2H-Azirine-based reagents for chemoselective bioconjugation at carboxyl residues inside live cells. J. Am. Chem. Soc. 142:6051–59
     [Google Scholar]
  34. 34.
    Abbasov ME, Kavanagh ME, Ichu TA, Lazear MR, Tao Y et al. 2021. A proteome-wide atlas of lysine-reactive chemistry. Nat. Chem. 13:1081–92
     [Google Scholar]
  35. 35.
    Kuljanin M, Mitchell DC, Schweppe DK, Gikandi AS, Nusinow DP et al. 2021. Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries. Nat. Biotechnol. 39:630–41
     [Google Scholar]
  36. 36.
    Forrest I, Parker CG. 2023. Proteome-wide fragment-based ligand and target discovery. Isr. J. Chem. 63:e202200098
     [Google Scholar]
  37. 37.
    Molina DM, Jafari R, Ignatushchenko M, Seki T, Larsson EA et al. 2013. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science 341:84–87
     [Google Scholar]
  38. 38.
    Savitski MM, Reinhard FB, Franken H, Werner T, Savitski MF et al. 2014. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346:1255784
     [Google Scholar]
  39. 39.
    Peuget S, Zhu J, Sanz G, Singh M, Gaetani M et al. 2020. Thermal proteome profiling identifies oxidative-dependent inhibition of the transcription of major oncogenes as a new therapeutic mechanism for select anticancer compounds. Cancer Res. 80:1538–50
     [Google Scholar]
  40. 40.
    Hwang HY, Kim TY, Szász MA, Dome B, Malm J et al. 2020. Profiling the protein targets of unmodified bio-active molecules with drug affinity responsive target stability and liquid chromatography/tandem mass spectrometry. Proteomics 20:e1900325
     [Google Scholar]
  41. 41.
    Huber KV, Olek KM, Müller AC, Tan CSH, Bennett KL et al. 2015. Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling. Nat. Methods 12:1055–57
     [Google Scholar]
  42. 42.
    Dziekan JM, Yu H, Chen D, Dai L, Wirjanata G et al. 2019. Identifying purine nucleoside phosphorylase as the target of quinine using cellular thermal shift assay. Sci. Transl. Med. 11:eaau3174
     [Google Scholar]
  43. 43.
    Perrin J, Werner T, Kurzawa N, Rutkowska A, Childs DD et al. 2020. Identifying drug targets in tissues and whole blood with thermal-shift profiling. Nat. Biotechnol. 38:303–8
     [Google Scholar]
  44. 44.
    Tolvanen TA. 2022. Current advances in CETSA. Front. Mol. Biosci. 9:866764
     [Google Scholar]
  45. 45.
    Brummelkamp TR, Fabius AW, Mullenders J, Madiredjo M, Velds A et al. 2006. An shRNA barcode screen provides insight into cancer cell vulnerability to MDM2 inhibitors. Nat. Chem. Biol. 2:202–6
     [Google Scholar]
  46. 46.
    Ngo VN, Davis RE, Lamy L, Yu X, Zhao H et al. 2006. A loss-of-function RNA interference screen for molecular targets in cancer. Nature 441:106–10
     [Google Scholar]
  47. 47.
    Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA et al. 2014. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343:84–87
     [Google Scholar]
  48. 48.
    Konermann S, Brigham MD, Trevino AE, Joung J, Abudayyeh OO et al. 2015. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517:583–88
     [Google Scholar]
  49. 49.
    Biering SB, Sarnik SA, Wang E, Zengel JR, Leist SR et al. 2022. Genome-wide bidirectional CRISPR screens identify mucins as host factors modulating SARS-CoV-2 infection. Nat. Genet. 54:1078–89
     [Google Scholar]
  50. 50.
    Deleted in proof
  51. 51.
    Neggers JE, Kwanten B, Dierckx T, Noguchi H, Voet A et al. 2018. Target identification of small molecules using large-scale CRISPR-Cas mutagenesis scanning of essential genes. Nat. Commun. 9:502
     [Google Scholar]
  52. 52.
    Szlachta K, Kuscu C, Tufan T, Adair SJ, Shang S et al. 2018. CRISPR knockout screening identifies combinatorial drug targets in pancreatic cancer and models cellular drug response. Nat. Commun. 9:4275
     [Google Scholar]
  53. 53.
    Deng X, Rong J, Wang L, Vasdev N, Zhang L et al. 2019. Chemistry for positron emission tomography: recent advances in 11C-, 18F-, 13N-, and 15O-labeling reactions. Angew. Chem. Int. Ed. Engl. 58:2580–605
     [Google Scholar]
  54. 54.
    Goud NS, Bhattacharya A, Joshi RK, Nagaraj C, Bharath RD, Kumar P. 2021. Carbon-11: radiochemistry and target-based PET molecular imaging applications in oncology, cardiology, and neurology. J. Med. Chem. 64:1223–59
     [Google Scholar]
  55. 55.
    Korat S, Bidesi NSR, Bonanno F, Di Nanni A, Hoang ANN et al. 2021. Alpha-synuclein PET tracer development—an overview about current efforts. Pharmaceuticals 14:847
     [Google Scholar]
  56. 56.
    Tang YZ, Booth TC, Bhogal P, Malhotra A, Wilhelm T. 2011. Imaging of primary central nervous system lymphoma. Clin. Radiol. 66:768–77
     [Google Scholar]
  57. 57.
    Barthel H, Seibyl J, Lammertsma AA, Villemagne VL, Sabri O. 2020. Exploiting the full potential of β-amyloid and tau PET imaging for drug efficacy testing. J. Nucl. Med. 61:1105–6
     [Google Scholar]
  58. 58.
    Wahl RL, Herman JM, Ford E. 2011. The promise and pitfalls of positron emission tomography and single-photon emission computed tomography molecular imaging-guided radiation therapy. Semin. Radiat. Oncol. 21:88–100
     [Google Scholar]
  59. 59.
    Pancholi K. 2012. A review of imaging methods for measuring drug release at nanometre scale: a case for drug delivery systems. Expert Opin. Drug. Deliv. 9:203–18
     [Google Scholar]
  60. 60.
    Liu J, Malekzadeh M, Mirian N, Song T-A, Liu C, Dutta J 2021. Artificial intelligence-based image enhancement in pet imaging: noise reduction and resolution enhancement. PET Clin. 16:553–76
     [Google Scholar]
  61. 61.
    Moncada R, Barkley D, Wagner F, Chiodin M, Devlin JC et al. 2020. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38:333–42
     [Google Scholar]
  62. 62.
    Galeano Niño JL, Wu H, LaCourse KD, Kempchinsky AG, Baryiames A et al. 2022. Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature 611:810–17
     [Google Scholar]
  63. 63.
    Fan X, Dong J, Zhong S, Wei Y, Wu Q et al. 2018. Spatial transcriptomic survey of human embryonic cerebral cortex by single-cell RNA-seq analysis. Cell Res. 28:730–45
     [Google Scholar]
  64. 64.
    Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E et al. 2019. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363:1463–67
     [Google Scholar]
  65. 65.
    Stickels RR, Murray E, Kumar P, Li J, Marshall JL et al. 2021. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39:313–19
     [Google Scholar]
  66. 66.
    Ginsberg SD, Elarova I, Ruben M, Tan F, Counts SE et al. 2004. Single-cell gene expression analysis: implications for neurodegenerative and neuropsychiatric disorders. Neurochem. Res. 29:1053–64
     [Google Scholar]
  67. 67.
    Raj A, van Oudenaarden A. 2008. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135:216–26
     [Google Scholar]
  68. 68.
    Liu Y, Yang M, Deng Y, Su G, Enninful A et al. 2020. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183:1665–81.e18
     [Google Scholar]
  69. 69.
    Vickovic S, Lötstedt B, Klughammer J, Mages S, Segerstolpe Å et al. 2022. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat. Commun. 13:795
     [Google Scholar]
  70. 70.
    Zou Y, Palte MJ, Deik AA, Li H, Eaton JK et al. 2019. A GPX4-dependent cancer cell state underlies the clear-cell morphology and confers sensitivity to ferroptosis. Nat. Commun. 10:1617
     [Google Scholar]
  71. 71.
    Ennishi D, Jiang A, Boyle M, Collinge B, Grande BM et al. 2019. Double-hit gene expression signature defines a distinct subgroup of germinal center B-cell-like diffuse large B-cell lymphoma. J. Clin. Oncol. 37:190–201
     [Google Scholar]
  72. 72.
    Moffitt JR, Lundberg E, Heyn H. 2022. The emerging landscape of spatial profiling technologies. Nat. Rev. Genet. 23:741–59
     [Google Scholar]
  73. 73.
    Larsson L, Frisen J, Lundeberg J. 2021. Spatially resolved transcriptomics adds a new dimension to genomics. Nat. Methods 18:15–18
     [Google Scholar]
  74. 74.
    Rao A, Barkley D, Franca GS, Yanai I. 2021. Exploring tissue architecture using spatial transcriptomics. Nature 596:211–20
     [Google Scholar]
  75. 75.
    Tang F, Barbacioru C, Wang Y, Nordman E, Lee C et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6:377–82
     [Google Scholar]
  76. 76.
    Shapiro E, Biezuner T, Linnarsson S. 2013. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14:618–30
     [Google Scholar]
  77. 77.
    Poulin J-F, Tasic B, Hjerling-Leffler J, Trimarchi JM, Awatramani R. 2016. Disentangling neural cell diversity using single-cell transcriptomics. Nat. Neurosci. 19:1131–41
     [Google Scholar]
  78. 78.
    Drokhlyansky E, Smillie CS, Van Wittenberghe N, Ericsson M, Griffin GK et al. 2020. The human and mouse enteric nervous system at single-cell resolution. Cell 182:1606–22.e23
     [Google Scholar]
  79. 79.
    Enge M, Arda HE, Mignardi M, Beausang J, Bottino R et al. 2017. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171:321–30.e14
     [Google Scholar]
  80. 80.
    Shin D, Lee W, Lee JH, Bang D. 2019. Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations. Sci. Adv. 5:eaav2249
     [Google Scholar]
  81. 81.
    Habib N, Li Y, Heidenreich M, Swiech L, Avraham-Davidi I et al. 2016. Div-Seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353:925–28
     [Google Scholar]
  82. 82.
    Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. 2013. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10:1213–18
     [Google Scholar]
  83. 83.
    Dixit A, Parnas O, Li B, Chen J, Fulco CP et al. 2016. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167:1853–66.e17
     [Google Scholar]
  84. 84.
    Palla G, Fischer DS, Regev A, Theis FJ. 2022. Spatial components of molecular tissue biology. Nat. Biotechnol. 40:308–18
     [Google Scholar]
  85. 85.
    Tian L, Chen F, Macosko EZ. 2023. The expanding vistas of spatial transcriptomics. Nat. Biotechnol. 41:773–82
     [Google Scholar]
  86. 86.
    Mund A, Brunner A-D, Mann M. 2022. Unbiased spatial proteomics with single-cell resolution in tissues. Mol. Cell 82:2335–49
     [Google Scholar]
  87. 87.
    Geier B, Sogin EM, Michellod D, Janda M, Kompauer M et al. 2020. Spatial metabolomics of in situ host–microbe interactions at the micrometre scale. Nat. Microbiol. 5:498–510
     [Google Scholar]
  88. 88.
    Ma S, Leng Y, Li X, Meng Y, Yin Z, Hang W. 2022. High spatial resolution mass spectrometry imaging for spatial metabolomics: advances, challenges, and future perspectives. Trends Anal. Chem. 159:116902
     [Google Scholar]
  89. 89.
    Djambazova KV, Klein DR, Migas LG, Neumann EK, Rivera ES et al. 2020. Resolving the complexity of spatial lipidomics using MALDI TIMS imaging mass spectrometry. Anal. Chem. 92:13290–97
     [Google Scholar]
  90. 90.
    Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P et al. 2018. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360:176–82
     [Google Scholar]
  91. 91.
    Marusyk A, Janiszewska M, Polyak K. 2020. Intratumor heterogeneity: the Rosetta stone of therapy resistance. Cancer Cell 37:471–84
     [Google Scholar]
  92. 92.
    González-Silva L, Quevedo L, Varela I. 2020. Tumor functional heterogeneity unraveled by scRNA-seq technologies. Trends Cancer 6:13–19
     [Google Scholar]
  93. 93.
    Zhang Y, Wang D, Peng M, Tang L, Ouyang J et al. 2021. Single-cell RNA sequencing in cancer research. J. Exp. Clin. Cancer Res. 40:81
     [Google Scholar]
  94. 94.
    Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM et al. 2014. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344:1396–401
     [Google Scholar]
  95. 95.
    Seferbekova Z, Lomakin A, Yates LR, Gerstung M. 2023. Spatial biology of cancer evolution. Nat. Rev. Genet 24:295–313
     [Google Scholar]
  96. 96.
    Wang Y, Ma S, Ruzzo WL. 2020. Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities. Sci. Rep. 10:3490
     [Google Scholar]
  97. 97.
    Ji AL, Rubin AJ, Thrane K, Jiang S, Reynolds DL et al. 2020. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182:497–514.e22
     [Google Scholar]
  98. 98.
    Anderson AC, Yanai I, Yates LR, Wang L, Swarbrick A et al. 2022. Spatial transcriptomics. Cancer Cell 40:895–900
     [Google Scholar]
  99. 99.
    McNamara KL, Caswell-Jin JL, Joshi R, Ma Z, Kotler E et al. 2021. Spatial proteomic characterization of HER2-positive breast tumors through neoadjuvant therapy predicts response. Nat. Cancer 2:400–13
     [Google Scholar]
  100. 100.
    Chen B, Ma L, Paik H, Sirota M, Wei W et al. 2017. Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets. Nat. Commun. 8:16022
     [Google Scholar]
  101. 101.
    Rotow J, Bivona TG. 2017. Understanding and targeting resistance mechanisms in NSCLC. Nat. Rev. Cancer 17:637–58
     [Google Scholar]
  102. 102.
    Sharma K, Schmitt S, Bergner CG, Tyanova S, Kannaiyan N et al. 2015. Cell type– and brain region–resolved mouse brain proteome. Nat. Neurosci. 18:1819–31
     [Google Scholar]
  103. 103.
    Ortiz C, Carlén M, Meletis K. 2021. Spatial transcriptomics: molecular maps of the mammalian brain. Annu. Rev. Neurosci. 44:547–62
     [Google Scholar]
  104. 104.
    Beauchamp A, Yee Y, Darwin BC, Raznahan A, Mars RB, Lerch JP. 2022. Whole-brain comparison of rodent and human brains using spatial transcriptomics. eLife 11:e79418
     [Google Scholar]
  105. 105.
    Deng Y, Bartosovic M, Ma S, Zhang D, Kukanja P et al. 2022. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609:375–83
     [Google Scholar]
  106. 106.
    Chen WT, Lu A, Craessaerts K, Pavie B, Sala Frigerio C et al. 2020. Spatial transcriptomics and in situ sequencing to study Alzheimer's disease. Cell 182:976–91.e19
     [Google Scholar]
  107. 107.
    Zeng H, Huang J, Zhou H, Meilandt WJ, Dejanovic B et al. 2023. Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in a mouse model of Alzheimer's disease. Nat. Neurosci. 26:430–46
     [Google Scholar]
  108. 108.
    Roth R, Kim S, Kim J, Rhee S. 2020. Single-cell and spatial transcriptomics approaches of cardiovascular development and disease. BMB Rep. 53:393–99
     [Google Scholar]
  109. 109.
    Martinez-Val A, Bekker-Jensen DB, Steigerwald S, Koenig C, Østergaard O et al. 2021. Spatial-proteomics reveals phospho-signaling dynamics at subcellular resolution. Nat. Commun. 12:7113
     [Google Scholar]
  110. 110.
    Mao Y, Wang X, Huang P, Tian R. 2021. Spatial proteomics for understanding the tissue microenvironment. Analyst 146:3777–98
     [Google Scholar]
  111. 111.
    Sheng M, Greenberg ME. 1990. The regulation and function of c-fos and other immediate early genes in the nervous system. Neuron 4:477–85
     [Google Scholar]
  112. 112.
    Salinas CBG, Lu TT-H, Gabery S, Marstal K, Alanentalo T et al. 2018. Integrated brain atlas for unbiased mapping of nervous system effects following liraglutide treatment. Sci. Rep. 8:10310
     [Google Scholar]
  113. 113.
    Davoudian PA, Shao LX, Kwan AC. 2023. Shared and distinct brain regions targeted for immediate early gene expression by ketamine and psilocybin. ACS Chem. Neurosci. 14:468–80
     [Google Scholar]
  114. 114.
    Kim CK, Ye L, Jennings JH, Pichamoorthy N, Tang DD et al. 2017. Molecular and circuit-dynamical identification of top-down neural mechanisms for restraint of reward seeking. Cell 170:1013–27.e14
     [Google Scholar]
  115. 115.
    Deguchi T, Iwanski MK, Schentarra E-M, Heidebrecht C, Schmidt L et al. 2023. Direct observation of motor protein stepping in living cells using MINFLUX. Science 379:1010–15
     [Google Scholar]
  116. 116.
    Jungmann R, Avendaño MS, Dai M, Woehrstein JB, Agasti SS et al. 2016. Quantitative super-resolution imaging with qPAINT. Nat. Methods 13:439–42
     [Google Scholar]
  117. 117.
    Wolff JO, Scheiderer L, Engelhardt T, Engelhardt J, Matthias J, Hell SW. 2023. MINFLUX dissects the unimpeded walking of kinesin-1. Science 379:1004–10
     [Google Scholar]
  118. 118.
    Scinto SL, Bilodeau DA, Hincapie R, Lee W, Nguyen SS et al. 2021. Bioorthogonal chemistry. Nat. Rev. Methods Primers 1:30
     [Google Scholar]
  119. 119.
    Prokop S, Abranyi-Balogh P, Barti B, Vamosi M, Zoldi M et al. 2021. PharmacoSTORM nanoscale pharmacology reveals cariprazine binding on Islands of Calleja granule cells. Nat. Commun. 12:6505
     [Google Scholar]
  120. 120.
    Bird RE, Lemmel SA, Yu X, Zhou QA. 2021. Bioorthogonal chemistry and its applications. Bioconjug. Chem. 32:2457–79
     [Google Scholar]
  121. 121.
    Tyler DS, Vappiani J, Cañeque T, Lam EY, Ward A et al. 2017. Click chemistry enables preclinical evaluation of targeted epigenetic therapies. Science 356:1397–401
     [Google Scholar]
  122. 122.
    Sun DE, Fan X, Shi Y, Zhang H, Huang Z et al. 2021. Click-ExM enables expansion microscopy for all biomolecules. Nat. Methods 18:107–13
     [Google Scholar]
  123. 123.
    Edgington LE, Verdoes M, Bogyo M. 2011. Functional imaging of proteases: recent advances in the design and application of substrate-based and activity-based probes. Curr. Opin. Chem. Biol. 15:798–805
     [Google Scholar]
  124. 124.
    Blum G, von Degenfeld G, Merchant MJ, Blau HM, Bogyo M. 2007. Noninvasive optical imaging of cysteine protease activity using fluorescently quenched activity-based probes. Nat. Chem. Biol. 3:668–77
     [Google Scholar]
  125. 125.
    Hu M, Li L, Wu H, Su Y, Yang PY et al. 2011. Multicolor, one- and two-photon imaging of enzymatic activities in live cells with fluorescently quenched activity-based probes (qABPs). J. Am. Chem. Soc. 133:12009–20
     [Google Scholar]
  126. 126.
    Yim JJ, Tholen M, Klaassen A, Sorger J, Bogyo M. 2017. Optimization of a protease activated probe for optical surgical navigation. Mol. Pharmaceutics 15:750–58
     [Google Scholar]
  127. 127.
    Deleted in proof
  128. 128.
    Verdoes M, Oresic Bender K, Segal E, van der Linden WA, Syed S et al. 2013. Improved quenched fluorescent probe for imaging of cysteine cathepsin activity. J. Am. Chem. Soc. 135:14726–30
     [Google Scholar]
  129. 129.
    Kato D, Boatright KM, Berger AB, Nazif T, Blum G et al. 2005. Activity-based probes that target diverse cysteine protease families. Nat. Chem. Biol. 1:33–38
     [Google Scholar]
  130. 130.
    Feng Y, Zhu S, Antaris AL, Chen H, Xiao Y et al. 2017. Live imaging of follicle stimulating hormone receptors in gonads and bones using near infrared II fluorophore. Chem. Sci. 8:3703–11
     [Google Scholar]
  131. 131.
    Deng H, Lei Q, Yang N, Dai S, Peng H et al. 2022. Expanded application of a photoaffinity probe to study epidermal growth factor receptor tyrosine kinase with functional activity. Anal. Chem. 94:10118–26
     [Google Scholar]
  132. 132.
    Kim E, Yang KS, Kohler RH, Dubach JM, Mikula H, Weissleder R. 2015. Optimized near-IR fluorescent agents for in vivo imaging of Btk expression. Bioconjug. Chem. 26:1513–18
     [Google Scholar]
  133. 133.
    Ofori LO, Withana NP, Prestwood TR, Verdoes M, Brady JJ et al. 2015. Design of protease activated optical contrast agents that exploit a latent lysosomotropic effect for use in fluorescence-guided surgery. ACS Chem. Biol. 10:1977–88
     [Google Scholar]
  134. 134.
    Kelderhouse LE, Chelvam V, Wayua C, Mahalingam S, Poh S et al. 2013. Development of tumor-targeted near infrared probes for fluorescence guided surgery. Bioconjug. Chem. 24:1075–80
     [Google Scholar]
  135. 135.
    Pang Z, Schafroth MA, Ogasawara D, Wang Y, Nudell V et al. 2022. In situ identification of cellular drug targets in mammalian tissue. Cell 185:1793–805.e17
     [Google Scholar]
  136. 136.
    Choi SW, Guan W, Chung K. 2021. Basic principles of hydrogel-based tissue transformation technologies and their applications. Cell 184:4115–36
     [Google Scholar]
  137. 137.
    Ueda HR, Erturk A, Chung K, Gradinaru V, Chedotal A et al. 2020. Tissue clearing and its applications in neuroscience. Nat. Rev. Neurosci. 21:61–79
     [Google Scholar]
  138. 138.
    Wang X, Allen WE, Wright MA, Sylwestrak EL, Samusik N et al. 2018. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361:eaat5691
     [Google Scholar]
  139. 139.
    Singh J, Petter RC, Baillie TA, Whitty A. 2011. The resurgence of covalent drugs. Nat. Rev. Drug. Discov. 10:307–17
     [Google Scholar]
  140. 140.
    De Vita E. 2020. 10 Years into the resurgence of covalent drugs. Future Med. Chem. 13:193–210
     [Google Scholar]
  141. 141.
    Boike L, Henning NJ, Nomura DK. 2022. Advances in covalent drug discovery. Nat. Rev. Drug. Discov. 21:881–98
     [Google Scholar]
  142. 142.
    Lawitz E, Mangia A, Wyles D, Rodriguez-Torres M, Hassanein T et al. 2013. Sofosbuvir for previously untreated chronic hepatitis C infection. N. Engl. J. Med. 368:1878–87
     [Google Scholar]
  143. 143.
    Owen DR, Allerton CMN, Anderson AS, Aschenbrenner L, Avery M et al. 2021. An oral SARS-CoV-2 Mpro inhibitor clinical candidate for the treatment of COVID-19. Science 374:1586–93
     [Google Scholar]
  144. 144.
    Nonaka H, Mino T, Sakamoto S, Oh JH, Watanabe Y et al. 2023. Revisiting PFA-mediated tissue fixation chemistry: FixEL enables trapping of small molecules in the brain to visualize their distribution changes. Chem 9:523–40
     [Google Scholar]
  145. 145.
    Pan S, Jang SY, Wang D, Liew SS, Li Z et al. 2017. A suite of “minimalist” photo-crosslinkers for live-cell imaging and chemical proteomics: case study with BRD4 inhibitors. Angew. Chem. Int. Ed. Engl. 56:11816–21
     [Google Scholar]
  146. 146.
    Nudell V, Wang Y, Pang Z, Lal NK, Huang M et al. 2022. HYBRiD: hydrogel-reinforced DISCO for clearing mammalian bodies. Nat. Methods 19:479–85
     [Google Scholar]
  147. 147.
    Pan C, Cai R, Quacquarelli FP, Ghasemigharagoz A, Lourbopoulos A et al. 2016. Shrinkage-mediated imaging of entire organs and organisms using uDISCO. Nat. Methods 13:859–67
     [Google Scholar]
  148. 148.
    Yang B, Treweek JB, Kulkarni RP, Deverman BE, Chen CK et al. 2014. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158:945–58
     [Google Scholar]
  149. 149.
    Reich DS, Arnold DL, Vermersch P, Bar-Or A, Fox RJ et al. 2021. Safety and efficacy of tolebrutinib, an oral brain-penetrant BTK inhibitor, in relapsing multiple sclerosis: a phase 2b, randomised, double-blind, placebo-controlled trial. Lancet Neurol. 20:729–38
     [Google Scholar]
  150. 150.
    Montalban X, Arnold DL, Weber MS, Staikov I, Piasecka-Stryczynska K et al. 2019. Placebo-controlled trial of an oral BTK inhibitor in multiple sclerosis. N. Engl. J. Med. 380:2406–17
     [Google Scholar]
  151. 151.
    Healy LM, Stratton JA, Kuhlmann T, Antel J. 2022. The role of glial cells in multiple sclerosis disease progression. Nat. Rev. Neurol. 18:237–48
     [Google Scholar]
  152. 152.
    Lee DSW, Rojas OL, Gommerman JL. 2021. B cell depletion therapies in autoimmune disease: advances and mechanistic insights. Nat. Rev. Drug. Discov. 20:179–99
     [Google Scholar]
  153. 153.
    Hanker AB, Brewer MR, Sheehan JH, Koch JP, Sliwoski GR et al. 2017. An acquired HER2T798I gatekeeper mutation induces resistance to neratinib in a patient with HER2 mutant–driven breast cancer. Cancer Discov. 7:575–85
     [Google Scholar]
  154. 154.
    Kerbrat A, Ferre JC, Fillatre P, Ronziere T, Vannier S et al. 2016. Acute neurologic disorder from an inhibitor of fatty acid amide hydrolase. N. Engl. J. Med. 375:1717–25
     [Google Scholar]
  155. 155.
    Byrd JC, Furman RR, Coutre SE, Flinn IW, Burger JA et al. 2013. Targeting BTK with ibrutinib in relapsed chronic lymphocytic leukemia. N. Engl. J. Med. 369:32–42
     [Google Scholar]
  156. 156.
    Wiczer TE, Levine LB, Brumbaugh J, Coggins J, Zhao Q et al. 2017. Cumulative incidence, risk factors, and management of atrial fibrillation in patients receiving ibrutinib. Blood Adv. 1:1739–48
     [Google Scholar]
  157. 157.
    Skelly DA, Squiers GT, McLellan MA, Bolisetty MT, Robson P et al. 2018. Single-cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart. Cell Rep. 22:600–10
     [Google Scholar]
  158. 158.
    Litviňuková M, Talavera-López C, Maatz H, Reichart D, Worth CL et al. 2020. Cells of the adult human heart. Nature 588:466–72
     [Google Scholar]
  159. 159.
    Xiao L, Salem JE, Clauss S, Hanley A, Bapat A et al. 2020. Ibrutinib-mediated atrial fibrillation attributable to inhibition of C-terminal Src kinase. Circulation 142:2443–55
     [Google Scholar]
/content/journals/10.1146/annurev-pharmtox-033123-123610
Loading
Deciphering Drug Targets and Actions with Single-Cell and Spatial Resolution
Annual Review of Pharmacology and Toxicology 64, 507 (2024); https://doi.org/10.1146/annurev-pharmtox-033123-123610
/content/journals/10.1146/annurev-pharmtox-033123-123610
/content/journals/10.1146/annurev-pharmtox-033123-123610
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error