Spatial & Single-cell Omics Immunology


The lab's core objective is to establish advanced AI tools that have three primary functions:

  1. Extraction of Spatial Features with Clinical Significance:

    The lab focuses on developing techniques that allow for the identification and extraction of spatial features within tissue samples that hold clinical relevance. By leveraging these AI-driven methods, researchers can uncover critical patterns and characteristics that contribute to the understanding of disease progression, treatment response, and patient outcomes.

  2. Prediction of Spatial Omics Signals from Histological H&E Images  virtual staining of cancer biomarkers:

    One of the lab's key areas of expertise lies in leveraging AI algorithms to predict various spatial omics signals directly from commonly available, low-cost histological H&E images. By doing so, researchers can accelerate the process of generating valuable insights from existing histopathological data. This approach saves time and resources that would otherwise be required for specialized, expensive spatial omics profiling techniques. 

  3. Integration of Multiple Spatial Omics Data: 

    The lab focuses on developing AI-based approaches to integrate and analyze multiple types of spatial omics data. By combining data from different sources, such as spatial transcriptomics, spatial proteomics, and spatial genomics, researchers can gain a comprehensive understanding of the complex interactions and dynamics within the tumor microenvironment (TME). This integration enables a more holistic characterization of the TME and facilitates the discovery of novel biomarkers and therapeutic targets.

    Overall, the lab's efforts aim to advance the field of spatial omics research by providing robust AI tools that enable the extraction of clinically significant spatial features, prediction of omics signals from routine histological images, and integration of diverse spatial omics data for a comprehensive understanding of the TME.





 Assistant Principal Investigator LAU Mai Chan   |    [View Bio]  
 Intern  ZHANG Marcia Qiao Yan
 Intern  FENG Xinyun


Selected Publications

* Denotes first authorship, # denoted corresponding authorship. h-index = 16; 1028 citations

  1. *Lau MC, Yi Y, Goh D, Cheung CCL, Tan B, Lim CTJ, Joseph CR, Wee F, Lee NJ, Lim X, Lim CJ, Leow WQ, Lee JY, Ng CYC, Bashiri H, Cheow PC, Chan CY, Koh YX, Tan TT, Kalmimuddin S, Tai WMD, Ng JL, Low GHJ, Lim KHT, Jin L, Yeong PSJ. Case Report: Understanding the Impact of Persistent Tissue-localization of SARS-CoV-2 on Immune Response Activity via Spatial Transcriptomic Analysis of Two Cancer Patients with COVID-19 Co-Morbidity. Front Immunol. 2022 Sep 12;13:978760. doi: 10.3389/fimmu.2022.978760. PMID: 36172383; PMCID: PMC9510984. (IF 8.787; 2 citations)

  2. Lee RY, Ng CW, Rajapakse MP, Ang N, Yeong J, #Lau MC. The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI. Front Oncol, 13, 1837. (IF 6.244; 0 citations)

  3. Lee RY, Wu Y, Goh D, Tan V, Ng CW, Lim JC, #Lau MC, Sheng JY. Application of Artificial Intelligence to In Vitro Tumor Modeling and Characterization of the Tumor Microenvironment. Adv. Healthc. Mater. 2023 Apr 15:2202457. (IF 11.092; 0 citations)

  4. Melissa Zhao, *Lau MC, Haruki K, Väyrynen J, Gurjao C, Väyrynen S, Dias Costa A, Borowsky J, Fujiyoshi K, Arima K, Hamada T, Lennerz J, Fuchs C, Nishihara R, Chan A, Ng K, Zhang X, Meyerhardt J, Song M, Wang M, Giannakis M, Nowak J, Yu KH, and Ugai T, Ogino S. Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data. NPJ Precis. Oncol. 7(1), 57.

  5. Chen H, Lau MC, Wong MT, Newell EW, Poidinger M, Chen JM. Cytofkit: A bioconductor package for an integrated mass cytometry data analysis pipeline. PLoS Comput Biol. 2016;12(9);e1005112. (IF 4.779; 296 citations)

  6. Väyrynen JP, Haruki K, *Lau MC, Väyrynen SA, Zhong R, Dias Costa A, Borowsky J, Zhao M, Fujiyoshi K, Arima K, Twombly TS, Kishikawa J, Gu S, Aminmozaffari S, Shi S, Baba Y, Akimoto N, Ugai T, Da Silva A, Guerriero JL, Song M, Wu K, Chan AT, Nishihara R, Fuchs CS, Meyerhardt JA, Giannakis M, Ogino S, and Nowak JA. The prognostic role of macrophage polarization in the colorectal cancer microenvironment. Cancer Immunol Res. 2021;9(1):8-19. (IF 12.020; 65 citations)

  7. Janela B, Patel AA, Lau MC, Goh CC, Msallam R, Kong WT, Fehlings M, Hubert S, Lum J, Simon Y, Malleret B, Zolezzi F, Chen J, Poidinger M, Satpathy AT, Briseno C, Wohn C, Malissen B, Murphy KM, Maini AA, Vanhoutte L, Guilliams M, Vial E, Hannequin L, Newell E, Ng LG, Musette P, Yona S, Rachinel FH, Ginhoux F. A subset of type I conventional dendritic cells controls cutaneous bacterial infections through VEGFa-mediated recruitment of neutrophils. Immunity. 2019;50:1069-1084;e8. (IF 43.47; 56 citations)

  8. Borowsky J, Haruki K, *Lau MC, Dias Costa A, Väyrynen JP, Ugai T, Arima K, da Silva A, Felt KD, Zhao M, Twombly TS, Fujiyoshi K, Väyrynen SA, Hamada T, Mima K, Bullman S, Ng K, Meyerhardt JA, Song M, Giovannucci EL, Wu K, Zhang X, Freeman GJ, Huttenhower C, Garrett WS, Chan AT, Leggett BA, Whitehall VLJ, Walker N, Brown I, Bettington M, Nishihara R, Fuchs CS, Lennerz JK, Giannakis M, Nowak JA, and Ogino S. Association of Fusobacterium nucleatum with Specific T Cell Subsets in the Colorectal Carcinoma Microenvironment. Clin Cancer Res. 2021;27(10):2816-2826. (IF 13.801; 33 citations)

  9. Väyrynen JP, *Lau MC, Haruki K, Väyrynen SA, Dias Costa A, Borowsky J, Zhao M, Fujiyoshi K, Arima K, Twombly TS, Kishikawa J, Gu S, Aminmozaffari S, Shi S, Baba Y, Akimoto N, Ugai T, da Silva A, Song M, Wu K, Chan AT, Nishihara R, Fuchs CS, Meyerhardt JA, Giannakis M, Ogino S, Nowak JA. Prognostic significance of immune cell populations identified by machine learning in colorectal cancer using routine hematoxylin & eosin stained sections. Clin Cancer Res. 2020; 26(16):4326-4338. (IF 13.801; 30 citations)

  10. Väyrynen JP, Haruki K, Väyrynen SA, *Lau MC, Dias Costa A, Borowsky J, Zhao M, Ugai T, Kishikawa J, Akimoto N, Zhong R, Shi S, Chang T-W, Fujiyoshi K, Arima K, Twombly TS, Da Silva A, Song M, Wu K, Zhang X, Chan AT, Nishihara R, Fuchs CS, Meyerhardt JA, Giannakis M, Ogino S, Nowak JA. Prognostic significance of myeloid immune cells and their spatial distribution in the colorectal cancer microenvironment. J Immunother Cancer. 2021;9(4): e002297. (IF 12.485; 12 citations)

  11. Ugai T, Väyrynen JP, *Lau MC, Borowsky J, Akimoto N, Väyrynen S, Zhao M, Zhong R, Haruki K, Dias Costa A, Fujiyoshi K, Arima K, Wu K, Chan AT, Cao Y, Song M, Fuchs CS, Wang M, Lennerz JK, Ng K, Meyerhardt JA, Giannakis M, Nowak JA, Ogino S.. Immune cell profiles in the tumor microenvironment of early-onset, intermediate-onset, and later-onset colorectal cancer. Cancer Immunol Immunother. 2022;71(4):933-942 (IF 5.442; 10 citations)

  12. *Lau MC, Srinivasan R. A hybrid CPU-graphics processing unit (GPU) approach for computationally efficient simulation-optimization. Comput. & Chem. Eng. 2016;87:49-62.