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B cell acute lymphoblastic leukemia (B-ALL) blasts hijack the bone marrow (BM) microenvironment to form chemoprotective leukemic BM niches facilitating chemoresistance and,ultimately,disease relapse. However,the ability to dissect these evolving,heterogeneous interactions among distinct B-ALL subtypes and their varying BM niches is limited with current in vivo methods. Here,we demonstrated an in vitro organotypic leukemia-on-a-chip" model to emulate the in vivo B-ALL BM pathology and comparatively studied the spatial and genetic heterogeneity of the BM niche in regulating B-ALL chemotherapy resistance. We revealed the heterogeneous chemoresistance mechanisms across various B-ALL cell lines and patient-derived samples. We showed that the leukemic perivascular endosteal and hematopoietic niche-derived factors maintain B-ALL survival and quiescence (e.g. CXCL12 cytokine signal VCAM-1/OPN adhesive signals and enhanced downstream leukemia-intrinsic NF-$\kappa$B pathway). Furthermore we demonstrated the preclinical use of our model to test niche-cotargeting regimens which may translate to patient-specific therapy screening and response prediction." View Publication

Human regulatory T (Treg) cells are essential for immune homeostasis. The transcription factor FOXP3 maintains Treg cell identity,yet the complete set of key transcription factors that control Treg cell gene expression remains unknown. Here,we used pooled and arrayed Cas9 ribonucleoprotein screens to identify transcription factors that regulate critical proteins in primary human Treg cells under basal and proinflammatory conditions. We then generated 54,424 single-cell transcriptomes from Treg cells subjected to genetic perturbations and cytokine stimulation,which revealed distinct gene networks individually regulated by FOXP3 and PRDM1,in addition to a network coregulated by FOXO1 and IRF4. We also discovered that HIVEP2,to our knowledge not previously implicated in Treg cell function,coregulates another gene network with SATB1 and is important for Treg cell-mediated immunosuppression. By integrating CRISPR screens and single-cell RNA-sequencing profiling,we have uncovered transcriptional regulators and downstream gene networks in human Treg cells that could be targeted for immunotherapies. View Publication

Detection and characterization of rare circulating tumor cells (CTCs) in patients' blood is important for the diagnosis and monitoring of cancer. The traditional way of counting CTCs via fluorescent images requires a series of tedious experimental procedures and often impacts the viability of cells. Here we present a method for label-free detection of CTCs from patient blood samples,by taking advantage of data analysis of bright field microscopy images. The approach uses the convolutional neural network,a powerful image classification and machine learning algorithm to perform label-free classification of cells detected in microscopic images of patient blood samples containing white blood cells and CTCs. It requires minimal data pre-processing and has an easy experimental setup. Through our experiments,we show that our method can achieve high accuracy on the identification of rare CTCs without the need for advanced devices or expert users,thus providing a faster and simpler way for counting and identifying CTCs. With more data becoming available in the future,the machine learning model can be further improved and can serve as an accurate and easy-to-use tool for CTC analysis. View Publication