Biology subjects

Ong, S. T.

Publications and source records attributed to Ong, S. T..

4 recordsLinked to original sources

bioRxiv2025

Single-cell transcriptomics-guided development of flow cytometric tests predicting chronic myeloid leukemia blast crisis transformation at chronic phase diagnosis

Clinical risk scores in chronic myeloid leukemia (CML) are inadequate for identifying chronic phase (CP) patients at high-risk of blast crisis (BC) progression. The lack of accurate predictive tests hamper timely interventions, including stem cell transplants, that are more effective in early disease. By interrogating a single cell atlas of primary imatinib resistance for a BC-like gene expression signature, we identify aberrant CD42A+ megakaryocytic and CD10+CD19+ lymphoid progenitor expansion, as well as STAT1- and IFN{gamma}-related inflammatory programs, as consistent features in the bone marrow and peripheral blood of CP patients at high-risk of BC transformation. We develop multi-color flow cytometry-based tests (MFC) to detect these features in CP patients at the time of diagnosis. Validating our MFC panels on a combined Australia-Singapore cohort comprising 28 CP patients, including 14 who underwent transformation, we demonstrate their ability to detect 100% of CP patients who transform with no false positives. Our findings highlight small populations of inflamed hematopoietic stem and progenitor cells, present at CP diagnosis, as powerful harbingers of future BC transformation. The ability of MFC panels to detect these cells at diagnosis support their inclusion as accurate risk-assessment tools to improve management of high-risk patients.

cancer biology↗

bioRxiv2021

Robust clustering and interpretation ofscRNA-seq data using reference componentanalysis

MotivationThe transcriptomic diversity of the hundreds of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Though clustering of cellular transcriptomes is the default technique for defining cell types and subtypes, single cell clustering can be strongly influenced by technical variation. In fact, the prevalent unsupervised clustering algorithms can cluster cells by technical, rather than biological, variation. ResultsCompared to de novo (unsupervised) clustering methods, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects. To leverage the advantages of supervised clustering, we present RCA2, a new, scalable, and broadly applicable version of our RCA algorithm. RCA2 provides a user-friendly framework for supervised clustering and downstream analysis of large scRNA-seq data sets. RCA2 can be seamlessly incorporated into existing algorithmic pipelines. It incorporates various new reference panels for human and mouse, supports generation of custom panels and uses efficient graph-based clustering and sparse data structures to ensure scalability. We demonstrate the applicability of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Importantly, RCA2 facilitates cell-type-specific QC, which we show is essential for accurate clustering of SC data from heterogeneous tissues. In the era of cohort-scale SC analysis, supervised clustering methods such as RCA2 will facilitate unified analysis of diverse SC datasets. AvailabilityRCA2 is implemented in R and is available at github.com/prabhakarlab/RCAv2

bioinformatics↗

bioRxiv2019

Identification of drugs for leukaemia differentiation therapy by network pharmacology

Acute leukaemias differ from their normal haematopoietic counterparts in their inability to differentiate. This phenomenon is thought to be the result of aberrant cellular reprogramming involving transcription factors (TFs). Here we leveraged on Mogrify, a network-based algorithm, to identify TFs and their gene regulatory networks that drive differentiation of the acute promyelocytic leukaemia (APL) cell line NB4 in response to ATRA (all-trans retinoic acid). We further integrated the detected TF regulatory networks with the Connectivity Map (CMAP) repository and recovered small molecule drugs which induce similar transcriptional changes. Our method outperformed standard approaches, retrieving ATRA as the top hit. Of the other drug hits, dimaprit and mebendazole enhanced ATRA-mediated differentiation in both parental NB4 and ATRA-resistant NB4-MR2 cells. Thus, we provide proof-of-principle of our network-based computational platform for drug discovery and repositioning in leukaemia differentiation therapy, which can be extended to other dysregulated disease states.

systems biology↗

bioRxiv2019

Histone acetylome-wide association study of tuberculosis

Host-cell chromatin changes are thought to play an important role in the pathogenesis of infectious diseases. Here, we describe the first histone acetylome-wide association study (HAWAS) of an infectious disease, based on genome-wide H3K27 acetylation profiling of peripheral granulocytes and monocytes from subjects with active Mycobacterium tuberculosis (Mtb) infection and healthy controls. We detected >2,000 differentially acetylated loci in either cell type in a Chinese discovery cohort, which were validated in a subsequent multi-ethnic cohort, thus demonstrating that HAWAS can be independently corroborated. Acetylation changes were correlated with differential gene expression in a third cohort. Differential acetylation was enriched near potassium channel genes, including KCNJ15, which modulated Akt-mTOR signaling and promoted Mtb clearance in vitro. We performed histone acetylation QTL analysis on the dataset and identified candidate causal variants for immune phenotypes. Our study serves as proof-of-principle for HAWAS to infer mechanisms of host response to pathogens.

immunology↗