Biology subjects

Yan, K.-K.

Publications and source records attributed to Yan, K.-K..

11 recordsLinked to original sources

bioRxiv2024

Spotiphy enables single-cell spatial whole transcriptomics across the entire section

Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole tissue sections1, but the approach remains dogged by the challenge of achieving single-cell resolution without sacrificing whole genome coverage2,3. Here we present Spotiphy (Spot imager with pseudo single-cell resolution histology), a novel computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. In evaluations with Alzheimers disease (AD) and normal mouse brains, Spotiphy delivers the most precise cellular compositions. For the first time, Spotiphy reveals novel astrocyte regional specification in mouse brains. It distinguishes sub-populations of DAM (Disease-Associated Microglia) located in different AD mouse brain regions. Spotiphy also identifies multiple spatial domains as well as changes in the patterns of tumor-tumor microenvironment interactions using human breast ST data. Spotiphy enables visualization of cell localization and gene expression in tissue sections, offering key insights into the function of complex biological systems.

systems biology↗

bioRxiv2023

scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data

The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as "hidden drivers", are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

systems biology↗

bioRxiv2021

The miR-424/503 cluster modulates Wnt/β-catenin signaling in the mammary epithelium by regulating the expression of the LRP6 co-receptor

During the female lifetime, the enlargement of the epithelial compartment dictated by the ovarian cycles is supported by a transient increase in the MaSC population. Notably, activation of Wnt/{beta}-catenin signaling is an important trigger for MaSC expansion. Here, we report that the miR-424/503 cluster is a novel modulator of canonical Wnt-signaling in the mammary epithelium that exerts its function by targeting the LRP6 co-receptor. Additionally, we show that the loss of this microRNA cluster is associated with breast cancers possessing high levels of Wnt/{beta}-catenin signaling.

cancer biology↗

bioRxiv2020

Interrogation of SLFN11 in pediatric sarcomas uncovers an unexpected biological role and a novel therapeutic approach to overcoming resistance to replicative stress

Pediatric sarcomas represent a heterogeneous group of malignancies that exhibit variable response to DNA damaging chemotherapy. Schlafen family member 11 protein (SLFN11) increases sensitivity to replicative stress, and SLFN11 gene silencing has been implicated as a common mechanism of drug resistance in tumors in adults. We found SLFN11 to be widely expressed in our cohort of pediatric sarcomas. In sarcoma cell lines, protein expression strongly correlated with response to the PARP inhibitor talazoparib (TAL) and the topoisomerase I inhibitor irinotecan (IRN), with SLFN11 knockout resulting in significant loss of sensitivity in vitro and in vivo. However, SLFN11 expression was not associated with favorable outcomes in a retrospective analysis of our patient cohort; instead, the protein was retained and promoted tumor growth and evasion. Furthermore, we show that pediatric sarcomas develop resistance to TAL and IRN through impaired intrinsic apoptosis, and that resistance can be reversed by selective inhibition of BCL-XL. Statement of SignificanceThe role of SLFN11 in pediatric sarcomas has not been thoroughly explored. In contrast to its activity in adult tumors, SLFN11 did not predict favorable outcomes in pediatric patients, was not silenced, and promoted tumor growth. Resistance to replicative stress in SLFN11-expressing sarcomas was reversed by selective inhibition of BCL-XL.

cancer biology↗

bioRxiv2020

miR-424(322) is a molecular switch controlling pro-inflammatory vs anti-inflammatory skin DC subset differentiation by modulating TGF-β signaling

TGF-{beta} family ligands are key regulators of dendritic cell (DC) differentiation and activation. Epidermal Langerhans cells (LCs) require TGF- {beta} family signaling for their differentiation and canonical TGF-{beta}1 signaling secures a non-activated LC state. LCs reportedly control skin inflammation and are replenished from peripheral blood monocytes, which also give rise to pro-inflammatory monocyte-derived DCs (moDCs). Among all the miRNAs differentially expressed in LC vs moDCs, we observed miR-424 to be strongly induced during moDC differentiation from monocytes. We discovered that miR-424 is required for moDC differentiation from human and murine precursor cells in vitro and for inflammation-associated moDC in vivo. Mechanistically we found that low levels of miR-424 facilitate TGF-{beta}1-dependent LC differentiation at the expense of moDC differentiation. Loss of miR-424 in monocyte/DC precursors resulted in the induction of TGF-{beta} pathway. Therefore, miR-424 plays a decisive role in anti-inflammatory LC vs pro-inflammatory moDC differentiation from monocytes, and its repression allows TGF-{beta} ligands to promote LC differentiation. Short summaryMonocytes give rise to two distinct DC subsets in skin inflammation, exhibiting opposite roles in inflammation. This study identified miR-424(322) as a molecular switch controlling pro-inflammatory (moDC) vs anti-inflammatory LC subset differentiation by modulating TGF-{beta} signaling.

immunology↗

bioRxiv2020

Single-cell analysis identifies TCF4 and ID3 as a molecular switch of mammary epithelial stem cell differentiation.

It is well known that the expansion of the mammary epithelium during the ovarian cycles in female mammals is supported by the transient increase in mammary epithelial stem cells (MaSCs). However, dissecting the molecular mechanisms that govern MaSC function and differentiation is poorly understood due to the lack of standardized methods for their identification and isolation. The development of robust single-cell mRNA sequencing () technologies and the computational methods to analyze them provides us with novel tools to approach the challenge of studying MaSCs in a completely unbiased way without. Here, we have performed the largest scRNA-seq analysis of individual mammary epithelial cells (~70,000 cells). Our study identified a distinct cell population presenting molecular features of MaSCs. Importantly, further purification and additional in-depth single-cell analysis of these cells revealed that they are not a fully homogenous entity. Instead, we identified three subpopulations representing early stages of lineage commitment. By tracking their molecular evolution through single-cell network analysis we found that one of these subpopulations represents bipotent MaSCs from which luminal and basal lineages diverge. Importantly, we also confirmed the presence of these cells in human mammary glands. Finally, through expression and network analysis studies, we have uncovered transcription factors that are activated early during lineage commitment. These data identified E2-2 (Tcf4) and ID3 as a potential molecular switch of mammary epithelial stem cell differentiation.

developmental biology↗

bioRxiv2019

An integrative ENCODE resource for cancer genomics

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

genomics↗

bioRxiv2018

A cross-organism framework for supervised enhancer prediction with epigenetic pattern recognition and targeted validation

Enhancers are important noncoding elements, but they have been traditionally hard to characterize experimentally. Only a few mammalian enhancers have been validated, making it difficult to train statistical models for their identification properly. Instead, postulated patterns of genomic features have been used heuristically for identification. The development of massively parallel assays allows for the characterization of large numbers of enhancers for the first time. Here, we developed a framework that uses Drosophila STARR-seq data to create shape-matching filters based on enhancer-associated meta-profiles of epigenetic features. We combined these features with supervised machine learning algorithms (e.g., support vector machines) to predict enhancers. We demonstrated that our model could be applied to predict enhancers in mammalian species (i.e., mouse and human). We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mouse and transduction-based reporter assays in human cell lines. Overall, the validations involved 153 enhancers in 6 mouse tissues and 4 human cell lines. The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription-factor binding patterns at predicted enhancers and promoters in human cell lines. We demonstrated that these patterns enable the construction of a secondary model effectively discriminating between enhancers and promoters.

bioinformatics↗

bioRxiv2017

Measuring the reproducibility and quality of Hi-C data

Hi-C is currently the most widely used assay to investigate the 3D organization of the genome and to study its role in gene regulation, DNA replication, and disease. However, Hi-C experiments are costly to perform and involve multiple complex experimental steps; thus, accurate methods for measuring the quality and reproducibility of Hi-C data are essential to determine whether the output should be used further in a study. Using real and simulated data, we profile the performance of several recently proposed methods for assessing reproducibility of population Hi-C data, including HiCRep, GenomeDISCO, HiC-Spector and QuASAR-Rep. By explicitly controlling noise and sparsity through simulations, we demonstrate the deficiencies of performing simple correlation analysis on pairs of matrices, and we show that methods developed specifically for Hi-C data produce better measures of reproducibility. We also show how to use established (e.g., ratio of intra to interchromosomal interactions) and novel (e.g., QuASAR-QC) measures to identify low quality experiments. In this work, we assess reproducibility and quality measures by varying sequencing depth, resolution and noise levels in Hi-C data from 13 cell lines, with two biological replicates each, as well as 176 simulated matrices. Through this extensive validation and benchmarking of Hi-C data, we describe best practices for reproducibility and quality assessment of Hi-C experiments. We make all software publicly available at http://github.com/kundajelab/3DChromatin_ReplicateQC to facilitate adoption in the community.

genomics↗

bioRxiv2016

MrTADFinder: A network modularity based approach to identify topologically associating domains in multiple resolutions

Genome-wide proximity ligation based assays such as Hi-C have revealed that eukaryotic genomes are organized into structural units called topologically associating domains (TADs). From a visual examination of the chromosomal contact map, however, it is clear that the organization of the domains is not simple or obvious. Instead, TADs exhibit various length scales and, in many cases, a nested arrangement. Here, by exploiting the resemblance between TADs in a chromosomal contact map and densely connected modules in a network, we formulate TAD identification as an optimization problem and propose an algorithm, MrTADFinder, to identify TADs from intra-chromosomal contact maps. MrTADFinder is based on the network-science concept of modularity. A key component of it is deriving an appropriate background model for contacts in a random chain, by numerically solving a set of matrix equations. The background model preserves the observed coverage of each genomic bin as well as the distance dependence of the contact frequency for any pair of bins exhibited by the empirical map. Also, by introducing a tunable resolution parameter, MrTADFinder provides a self-consistent approach for identifying TADs at different length scales, hence the acronym \"Mr\" standing for Multiple Resolutions. We then apply MrTADFinder to various Hi-C datasets. The identified domains are marked by boundary signatures in chromatin marks and transcription factor (TF) that are consistent with earlier work. Moreover, by calling TADs at different length scales, we observe that boundary signatures change with resolution, with different chromatin features having different characteristic length scales. Furthermore, we report an enrichment of HOT regions near TAD boundaries and investigate the role of different TFs in determining boundaries at various resolutions. To further explore the interplay between TADs and epigenetic marks, we examine how somatic mutations are distributed across boundaries (as tumor mutational burden is known to be coupled to chromatin structure), finding a clear stepwise pattern. Overall, MrTADFinder provides a novel computational framework to explore the multi-scale structures in Hi-C contact maps.\n\nAuthor SummaryThe accommodation of the roughly 2m of DNA in the nuclei of mammalian cells results in an intricate structure, in which the topologically associating domains (TADs) formed by densely interacting genomic regions emerge as a fundamental structural unit. Identification of TADs is essential for understanding the role of 3D genome organization in gene regulation. By viewing the chromosomal contact map as a network, TADs correspond to the densely connected regions in the network. Motivated by this mapping, we propose a novel method, MrTADFinder, to identify TADs based on the concept of modularity in network science. Using MrTADFinder, we identify domains at various resolutions, and further explore the interplay between domains and other chromatin features like transcription factors binding and histone modifications at different resolutions. Overall, MrTADFinder provides a new computational framework to investigate the multiple length scales that are built inside the organization of the genome.

genomics↗

bioRxiv2016

HiC-Spector: A matrix library for spectral and reproducibility analysis of Hi-C contact maps

SummaryGenome-wide proximity ligation based assays like Hi-C have opened a window to the 3D organization of the genome. In so doing, they present data structures that are different from conventional 1D signal tracks. To exploit the 2D nature of Hi-C contact maps, matrix techniques like spectral analysis are particularly useful. Here, we present HiC-spector, a collection of matrix-related functions for analyzing Hi-C contact maps. In particular, we introduce a novel reproducibility metric for quantifying the similarity between contact maps based on spectral decomposition. The metric successfully separates contact maps mapped from Hi-C data coming from biological replicates, pseudo-replicates and different cell types.\n\nAvailabilitySource code in Julia and the documentation of HiC-spector can be freely obtained at https://github.com/gersteinlab/HiC_spector\n\nContactpi@gersteinlab.org

bioinformatics↗