Dr. Tan is a computational biologist who develops computational tools to quantitatively assess cell identity, improve stem cell engineering, and understand cancer heterogeneity. As a Ph.D. student, Dr. Tan routinely performs computational and quantitative analysis on scRNA-seq data, which has resulted in several publications. Currently, at her postdoctoral position, Dr. Tan integrated single-cell omics with multiplexed image data to understand high dimensional tissue architecture in cancer. Dr.Tan's long-term aims are to integrate multi-omics to understand how different cell types and their interactions contribute to development and disease.
Bachelor of Science, Chinese University of Hong Kong (2014)
Doctor of Philosophy, Johns Hopkins University (2021)
BSc(Hon), The Chinese University of Hong Kong, Cell and Molecular Biology (2014)
PhD, Johns Hopkins University, Computational Biology (2021)
Garry Nolan, Postdoctoral Faculty Sponsor
Annotation of spatially resolved single-cell data with STELLAR.
Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset and discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6million spatially resolved single cells with dramatic time savings.
View details for DOI 10.1038/s41592-022-01651-8
View details for PubMedID 36280720
Gene Regulatory Network Analysis and Engineering Directs Development and Vascularization of Multilineage Human Liver Organoids
2021; 12 (1): 41-+
Pluripotent stem cell (PSC)-derived organoids have emerged as novel multicellular models of human tissue development but display immature phenotypes, aberrant tissue fates, and a limited subset of cells. Here, we demonstrate that integrated analysis and engineering of gene regulatory networks (GRNs) in PSC-derived multilineage human liver organoids direct maturation and vascular morphogenesis in vitro. Overexpression of PROX1 and ATF5, combined with targeted CRISPR-based transcriptional activation of endogenous CYP3A4, reprograms tissue GRNs and improves native liver functions, such as FXR signaling, CYP3A4 enzymatic activity, and stromal cell reactivity. The engineered tissues possess superior liver identity when compared with other PSC-derived liver organoids and show the presence of hepatocyte, biliary, endothelial, and stellate-like cell populations in single-cell RNA-seq analysis. Finally, they show hepatic functions when studied in vivo. Collectively, our approach provides an experimental framework to direct organogenesis in vitro by systematically probing molecular pathways and transcriptional networks that promote tissue development.
View details for DOI 10.1016/j.cels.2020.11.002
View details for Web of Science ID 000610099400004
View details for PubMedID 33290741
View details for PubMedCentralID PMC8164844
Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data.
Frontiers in immunology
2021; 12: 727626
Multiplexed imaging is a recently developed and powerful single-cell biology research tool. However, it presents new sources of technical noise that are distinct from other types of single-cell data, necessitating new practices for single-cell multiplexed imaging processing and analysis, particularly regarding cell-type identification. Here we created single-cell multiplexed imaging datasets by performing CODEX on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. After cell segmentation, we implemented five different normalization techniques crossed with four unsupervised clustering algorithms, resulting in 20 unique cell-type annotations for the same dataset. We generated two standard annotations: hand-gated cell types and cell types produced by over-clustering with spatial verification. We then compared these annotations at four levels of cell-type granularity. First, increasing cell-type granularity led to decreased labeling accuracy; therefore, subtle phenotype annotations should be avoided at the clustering step. Second, accuracy in cell-type identification varied more with normalization choice than with clustering algorithm. Third, unsupervised clustering better accounted for segmentation noise during cell-type annotation than hand-gating. Fourth, Z-score normalization was generally effective in mitigating the effects of noise from single-cell multiplexed imaging. Variation in cell-type identification will lead to significant differential spatial results such as cellular neighborhood analysis; consequently, we also make recommendations for accurately assigning cell-type labels to CODEX multiplexed imaging.
View details for DOI 10.3389/fimmu.2021.727626
View details for PubMedID 34484237
Transcriptome Dynamics of Hematopoietic Stem Cell Formation Revealed Using a Combinatorial Runx1 and Ly6a Reporter System
STEM CELL REPORTS
2020; 14 (5): 956-971
Studies of hematopoietic stem cell (HSC) development from pre-HSC-producing hemogenic endothelial cells (HECs) are hampered by the rarity of these cells and the presence of other cell types with overlapping marker expression profiles. We generated a Tg(Runx1-mKO2; Ly6a-GFP) dual reporter mouse to visualize hematopoietic commitment and study pre-HSC emergence and maturation. Runx1-mKO2 marked all intra-arterial HECs and hematopoietic cluster cells (HCCs), including pre-HSCs, myeloid- and lymphoid progenitors, and HSCs themselves. However, HSC and lymphoid potential were almost exclusively found in reporter double-positive (DP) cells. Robust HSC activity was first detected in DP cells of the placenta, reflecting the importance of this niche for (pre-)HSC maturation and expansion before the fetal liver stage. A time course analysis by single-cell RNA sequencing revealed that as pre-HSCs mature into fetal liver stage HSCs, they show signs of interferon exposure, exhibit signatures of multi-lineage differentiation gene expression, and develop a prolonged cell cycle reminiscent of quiescent adult HSCs.
View details for DOI 10.1016/j.stemcr.2020.03.020
View details for Web of Science ID 000533148900015
View details for PubMedID 32302558
View details for PubMedCentralID PMC7220988
Ligustrazine Prevents Intervertebral Disc Degeneration via Suppression of Aberrant TGF beta Activation in Nucleus Pulposus Cells
BIOMED RESEARCH INTERNATIONAL
2019; 2019: 5601734
Aberrant transforming growth factor β (TGFβ) activation is detrimental to both nucleus pulposus (NP) cells and cartilage endplates (CEPs), which can lead to intervertebral disc degeneration (IDD). Ligustrazine (LIG) reduces the expression of inflammatory factors and TGFβ1 in hypertrophic CEP to prevent IDD. In this study, we investigate the effects of LIG on NP cells and the TGFβ signaling.LIG was injected to the lumbar spinal instability (LSI) mouse model. The effect of LIG was evaluated by intervertebral disc (IVD) score in the LSI mouse model. The expression of activated TGFβ was examined using immunostaining with pSmad2/3 antibody. The upright posture (UP) rat model was also treated and evaluated in the same manner to assess the effect of LIG. In ex vivo study, IVDs from four-week old mice were isolated and treated with 10-5, 10-6, and 10-7 M of LIG. We used western blot to detect activated TGFβ expression. TGFβ-treated human nucleus pulposus cells (HNPCs) were cotreated with optimized dose of LIG in vitro. Immunofluorescence staining was performed to determine pSmad2/3, connective tissue growth factor (CCN2), and aggrecan (ACAN) expression levels.IVD score and the percentage of pSmad2/3+ NP cells were low in LIG-treated LSI mice in comparison with LSI mice, but close to the levels in the Sham group. Similarly, LIG reduced the overexpression of TGFβ1 in NP cells. The inhibitory effect of LIG was dose dependent. A dose of 10-5 M LIG not only strongly attenuated Smad2/3 phosphorylation in TGFβ-treated IVD ex vivo but also suppressed pSmad2/3, CCN2, and ACAN expression in TGFβ-treated NP cells in vitro.LIG prevents IDD via suppression of TGFβ overactivation in NP cells.
View details for DOI 10.1155/2019/5601734
View details for Web of Science ID 000503421400003
View details for PubMedID 31886227
View details for PubMedCentralID PMC6914881
SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species
2019; 9 (2): 207-+
Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.
View details for DOI 10.1016/j.cels.2019.06.004
View details for Web of Science ID 000483697600008
View details for PubMedID 31377170
View details for PubMedCentralID PMC6715530
SCD1 and SCD2 Form a Complex That Functions with the Exocyst and RabE1 in Exocytosis and Cytokinesis
2017; 29 (10): 2610-2625
Although exocytosis is critical for the proper trafficking of materials to the plasma membrane, relatively little is known about the mechanistic details of post-Golgi trafficking in plants. Here, we demonstrate that the DENN (Differentially Expressed in Normal and Neoplastic cells) domain protein STOMATAL CYTOKINESIS DEFECTIVE1 (SCD1) and SCD2 form a previously unknown protein complex, the SCD complex, that functionally interacts with subunits of the exocyst complex and the RabE1 family of GTPases in Arabidopsis thaliana Consistent with a role in post-Golgi trafficking, scd1 and scd2 mutants display defects in exocytosis and recycling of PIN2-GFP. Perturbation of exocytosis using the small molecule Endosidin2 results in growth inhibition and PIN2-GFP trafficking defects in scd1 and scd2 mutants. In addition to the exocyst, the SCD complex binds in a nucleotide state-specific manner with Sec4p/Rab8-related RabE1 GTPases and overexpression of wild-type RabE1 rescues scd1 temperature-sensitive mutants. Furthermore, SCD1 colocalizes with the exocyst subunit, SEC15B, and RabE1 at the cell plate and in distinct punctae at or near the plasma membrane. Our findings reveal a mechanism for plant exocytosis, through the identification and characterization of a protein interaction network that includes the SCD complex, RabE1, and the exocyst.
View details for DOI 10.1105/tpc.17.00409
View details for Web of Science ID 000414861100025
View details for PubMedID 28970336
View details for PubMedCentralID PMC5774579
Assessment of engineered cells using CellNet and RNA-seq
2017; 12 (5): 1089-1102
CellNet is a computational platform designed to assess cell populations engineered by either directed differentiation of pluripotent stem cells (PSCs) or direct conversion, and to suggest specific hypotheses to improve cell fate engineering protocols. CellNet takes as input gene expression data and compares them with large data sets of normal expression profiles compiled from public sources, in regard to the extent to which cell- and tissue-specific gene regulatory networks are established. CellNet was originally designed to work with human or mouse microarray expression data for 21 cell or tissue (C/T) types. Here we describe how to apply CellNet to RNA-seq data and how to build a completely new CellNet platform applicable to, for example, other species or additional cell and tissue types. Once the raw data have been preprocessed, running CellNet takes only several minutes, whereas the time required to create a completely new CellNet is several hours.
View details for DOI 10.1038/nprot.2017.022
View details for Web of Science ID 000400371100009
View details for PubMedID 28448485
View details for PubMedCentralID PMC5765439
Understanding development and stem cells using single cell-based analyses of gene expression
2017; 144 (1): 17-32
In recent years, genome-wide profiling approaches have begun to uncover the molecular programs that drive developmental processes. In particular, technical advances that enable genome-wide profiling of thousands of individual cells have provided the tantalizing prospect of cataloging cell type diversity and developmental dynamics in a quantitative and comprehensive manner. Here, we review how single-cell RNA sequencing has provided key insights into mammalian developmental and stem cell biology, emphasizing the analytical approaches that are specific to studying gene expression in single cells.
View details for DOI 10.1242/dev.133058
View details for Web of Science ID 000393454900005
View details for PubMedID 28049689
View details for PubMedCentralID PMC5278625
MONENSIN SENSITIVITY1 (MON1)/CALCIUM CAFFEINE ZINC SENSITIVITY1 (CCZ1)-Mediated Rab7 Activation Regulates Tapetal Programmed Cell Death and Pollen Development
2017; 173 (1): 206-218
Programmed cell death (PCD)-triggered degradation of plant tapetum is essential for microspore development and pollen coat formation; however, little is known about the cellular mechanism regulating tapetal PCD Here, we demonstrate that Rab7-mediated vacuolar transport of tapetum degradation-related cysteine proteases is crucial for tapetal PCD and pollen development in Arabidopsis (Arabidopsis thaliana), with the following evidence: (1) The monensin sensitivity1 (mon1) mutants, which are defective in Rab7 activation, showed impaired male fertility due to a combined defect in both tapetum and male gametophyte development. (2) In anthers, MON1 showed preferential high level expression in tapetal cell layers and pollen. (3) The mon1 mutants exhibited delayed tapetum degeneration and tapetal PCD, resulting in abnormal pollen coat formation and decreased male fertility. (4) MON1/CALCIUM CAFFEINE ZINC SENSITIVITY1 (CCZ1)-mediated Rab7 activation was indispensable for vacuolar trafficking of tapetum degradation-related cysteine proteases, supporting that PCD-triggered tapetum degeneration requires Rab7-mediated vacuolar trafficking of these cysteine proteases. (5) MON1 mutations also resulted in defective pollen germination and tube growth. Taken together, tapetal PCD and pollen development require successful MON1/CCZ1-mediated vacuolar transport in Arabidopsis.
View details for DOI 10.1104/pp.16.00988
View details for Web of Science ID 000394135800018
View details for PubMedID 27799422
View details for PubMedCentralID PMC5210713
- MON1/CCZ1-mediated Rab7 activation regulates tapetal PCD and pollen development in Arabidopsis Plant Physiology 2017
Valproate-Induced Neurodevelopmental Deficits in Xenopus laevis Tadpoles
JOURNAL OF NEUROSCIENCE
2015; 35 (7): 3218-3229
Autism spectrum disorder (ASD) is increasingly thought to result from low-level deficits in synaptic development and neural circuit formation that cascade into more complex cognitive symptoms. However, the link between synaptic dysfunction and behavior is not well understood. By comparing the effects of abnormal circuit formation and behavioral outcomes across different species, it should be possible to pinpoint the conserved fundamental processes that result in disease. Here we use a novel model for neurodevelopmental disorders in which we expose Xenopus laevis tadpoles to valproic acid (VPA) during a critical time point in brain development at which neurogenesis and neural circuit formation required for sensory processing are occurring. VPA is a commonly prescribed antiepileptic drug with known teratogenic effects. In utero exposure to VPA in humans or rodents results in a higher incidence of ASD or ASD-like behavior later in life. We find that tadpoles exposed to VPA have abnormal sensorimotor and schooling behavior that is accompanied by hyperconnected neural networks in the optic tectum, increased excitatory and inhibitory synaptic drive, elevated levels of spontaneous synaptic activity, and decreased neuronal intrinsic excitability. Consistent with these findings, VPA-treated tadpoles also have increased seizure susceptibility and decreased acoustic startle habituation. These findings indicate that the effects of VPA are remarkably conserved across vertebrate species and that changes in neural circuitry resulting from abnormal developmental pruning can cascade into higher-level behavioral deficits.
View details for DOI 10.1523/JNEUROSCI.4050-14.2015
View details for Web of Science ID 000349992800034
View details for PubMedID 25698756
View details for PubMedCentralID PMC4331635