Life Sciences Commons^™

Scper: A Rigorous Computational Approach To Determine Cellular Subtypes In Tumors Aligned With Cancer Phenotypes From Total Rna Sequencing, Bingrui Li, Xiaobo Zhou, Raghu Kalluri

Faculty, Staff and Student Publications

Single-cell RNA sequencing (scRNA-seq) is a powerful technique for understanding cellular diversity, but processing large patient cohorts to identify phenotype-associated cell populations remains challenging. Here, scPER (Estimating cell Proportions using single-cell RNA-seq Reference), a rigorous approach combining adversarial autoencoder and extreme gradient boosting to estimate tumor microenvironment cell compositions and identify phenotype-associated subclusters for bulk RNA-seq samples. Integrating scRNA-seq datasets from diverse studies, scPER constructed comprehensive reference panels and disentangled confounders from true signals. scPER achieved superior accuracy in cellular proportion estimation compared to CIBERSORTx, BayesPrism, Scaden, MuSiC, SCDC, DeSide and ReCIDE. It showed high accuracy in predicting metastatic melanoma …

Celltra: Aligning Cell Names With Gene Expression Via A Pathway-Informed Transformer, Zhao Li, Zaiyi Zheng, Rongbin Li, Wenbo Chen, Yuntao Yang, Meer A Ali, Jundong Li, W Jim Zheng

Faculty, Staff and Student Publications

Motivation: Single-cell RNA sequencing (scRNA-Seq) technology enables detailed exploration of gene expression at the individual cell level, crucial for annotating cell types and understanding cellular diversity. Traditional methods for cell type annotation often rely on marker genes and manual labeling, posing challenges due to low data quality and incomplete reference datasets.

Results: We developed CeLLTra, a novel contrastive learning framework that leverages a Transformer-based model integrating biological pathway information to group genes into super tokens, effectively capturing comprehensive gene expression from scRNA-Seq data. By combining this pathway-informed Transformer with a pretrained domain-specific language model, CeLLTra accurately aligns cell-type annotations with …

Benchmarking Dna Foundation Models For Genomic And Genetic Tasks, Haonan Feng, Lang Wu, Bingxin Zhao, Chad Huff, Jianjun Zhang, Jia Wu, Lifeng Lin, Peng Wei, Chong Wu

Faculty, Staff and Student Publications

The rapid evolution of DNA foundation models promises to revolutionize genomics, yet comprehensive evaluations are lacking. Here, we present a comprehensive, unbiased benchmark of five models (DNABERT-2, Nucleotide Transformer V2, HyenaDNA, Caduceus-Ph, and GROVER) across diverse genomic and genetic tasks including sequence classification, gene expression prediction, variant effect quantification, and topologically associating domain (TAD) region recognition, using zero-shot embeddings. Our analysis reveals that mean token embedding consistently and significantly improves sequence classification performance, outperforming other pooling strategies. Model performance varies among tasks and datasets; while general purpose DNA foundation models showed competitive performance in pathogenic variant identification, they were less …

Genome Sequencing Reveals The Impact Of Pseudoexons In Rare Genetic Disease, Georgia Pitsava, Megan Hawley, Light Auriga, Ivan De Dios, Arthur Ko, Sofia Marmolejos, Miguel Almalvez, Ingrid Chen, Kaylee Scozzaro, Jianhua Zhao, Rebekah Barrick, Nicholas Ah Mew, Vincent A Fusaro, Jonathan Lotempio, Matthew Taylor, Luisa Mestroni, Sharon Graw, Dianna Milewicz, Dongchuan Guo, David R Murdock, Kinga M Bujakowska, Changrui Xiao, Emmanuèle C Délot, Seth I Berger, Eric Vilain

Faculty, Staff and Student Publications

Purpose: Advancements in sequencing technologies have significantly improved clinical genetic testing; yet, the diagnostic yield remains around 30% to 40%. Emerging technologies are now being deployed to address the remaining diagnostic gap.

Methods: We tested whether short-read genome sequencing could increase the diagnostic yield in individuals enrolled into the UCI-GREGoR research study, who had suspected Mendelian conditions and prior inconclusive testing. Two other collaborative research cohorts, focused on aortopathy and dilated cardiomyopathy, consisted of individuals who were undiagnosed but had not undergone harmonized prior testing.

Results: We sequenced 353 families (754 participants) and found a molecular diagnosis in 54 (15.3%) …

Comparison Of Imaging Based Single-Cell Resolution Spatial Transcriptomics Profiling Platforms Using Formalin-Fixed Paraffin-Embedded Tumor Samples, Nejla Ozirmak Lermi, Max Molina Ayala, Sharia Hernandez, Wei Lu, Khaja Khan, Alejandra Serrano, Idania Lubo, Leticia Hamana, Katarzyna Tomczak, Sean Barnes, Jinzhuang Dou, Qingnan Liang, Rti Team, Maria Gabriela Raso, Ximing Tang, Mei Jiang, Beatriz Sanchez-Espiridion, Annikka Weissferdt, John Heymach, Jianjun Zhang, Boris Sepesi, Tina Cascone, Anne Tsao, Mehmet Altan, Reza Mehran, Don Gibbons, Ignacio Wistuba, Cara Haymaker, Ken Chen, Luisa M Solis Soto

Faculty, Staff and Student Publications

Imaging-based spatial transcriptomics (ST) is evolving as a pivotal technology in studying tumor biology and associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. We use serial 5 μm sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma samples in tissue microarrays to compare the performance of the ST platforms (CosMx, MERFISH, and Xenium (uni/multi-modal)) in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx, and hematoxylin and eosin staining data. In addition to an objective assessment of automatic cell segmentation and phenotyping, we …

Rna Sequencing And Immunohistochemistry Jointly Improve Tumor Biomarker Interpretation, Vladimir Kushnarev, Danil Stupichev, Suren Davitavyan, Kirill Kriukov, Basavaraja U Shanthappa, Anna Butusova, Sofia Menshikova, Anna Belozerova, Anastasia Shvyrkova, Arina Tkachuk, Olga Khatenkova, Linda Balabanian, Ekaterina Postovalova, Jochen K Lennerz, Funda Meric-Bernstam, Alexander Bagaev

Faculty, Staff and Student Publications

This study aimed to assess the correlation between RNA sequencing (RNA-seq) and immunohistochemistry (IHC) in detecting key cancer biomarkers across solid tumors, and then, to establish RNA-seq thresholds that accurately reflect clinical IHC classifications. Expression levels of nine biomarkers-ESR1, PGR, AR, MKI67, ERBB2, CD274, CDX2, KRT7, and KRT20-were analyzed in 365 formalin-fixed, paraffin-embedded samples from breast, lung, gastrointestinal, and other solid carcinomas. Correlations between RNA-seq data and IHC scores were determined using Spearman's correlation coefficients, with RNA-seq cut-offs established to distinguish positive from negative IHC scores. The results revealed strong correlations for most biomarkers, with coefficients ranging from 0.53 to …

Long-Read Single-Cell Rna Sequencing Enables The Study Of Cancer Subclone-Specific Genotypes And Phenotypes In Chronic Lymphocytic Leukemia, Gage S Black, Xiaomeng Huang, Yi Qiao, Philip Moos, Deepa Sampath, Deborah M Stephens, Jennifer A Woyach, Gabor T Marth

Faculty, Staff and Student Publications

Bruton tyrosine kinase (BTK) inhibitors are effective for the treatment of chronic lymphocytic leukemia (CLL) due to BTK's role in B cell survival and proliferation. Treatment resistance is most commonly caused by the emergence of the hallmark BTKC481S mutation that inhibits drug binding. In this study, we aimed to investigate cancer subclones harboring a BTKC481S mutation and identify cells with co-occurring CLL driver mutations. In addition, we sought to determine whether BTK-mutated subclones exhibit distinct transcriptomic behavior when compared to other cancer subclones. To achieve these goals, we use scBayes, which integrates bulk DNA sequencing and single-cell …

Crowd-Sourced Benchmarking Of Single-Sample Tumor Subclonal Reconstruction, Adriana Salcedo, Maxime Tarabichi, Alex Buchanan, Shadrielle M G Espiritu, Hongjiu Zhang, Kaiyi Zhu, Tai-Hsien Ou Yang, Ignaty Leshchiner, Dimitris Anastassiou, Yuanfang Guan, Gun Ho Jang, Mohammed F E Mootor, Kerstin Haase, Amit G Deshwar, William Zou, Imaad Umar, Stefan Dentro, Jeff A Wintersinger, Kami Chiotti, Jonas Demeulemeester, Clemency Jolly, Lesia Sycza, Minjeong Ko, Pcawg Evolution And Heterogeneity Working Group, Smc-Het Participants, David C Wedge, Quaid D Morris, Kyle Ellrott, Peter Van Loo, Paul C Boutros

Faculty, Staff and Student Publications

Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC-TCGA (International Cancer Genome Consortium-The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability …

Scupa: Single-Cell Unified Polarization Assessment Of Immune Cells Using The Single-Cell Foundation Model, Wendao Liu, Zhongming Zhao

Faculty, Staff and Student Publications

MOTIVATION: Immune cells undergo cytokine-driven polarization in response to diverse stimuli, altering their transcriptional profiles and functional states. This dynamic process is central to immune responses in health and diseases, yet a systematic approach to assess cytokine-driven polarization in single-cell RNA sequencing data has been lacking.

RESULTS: To address this gap, we developed single-cell unified polarization assessment (Scupa), the first computational method for comprehensive immune cell polarization assessment. Scupa leverages data from the Immune Dictionary, which characterizes cytokine-driven polarization states across 14 immune cell types. By integrating cell embeddings from the single-cell foundation model Universal Cell Embeddings, Scupa effectively identifies …

Seq-Scope: Repurposing Illumina Sequencing Flow Cells For High-Resolution Spatial Transcriptomics, Yongsung Kim, Weiqiu Cheng, Chun-Seok Cho, Yongha Hwang, Yichen Si, Anna Park, Mitchell Schrank, Jer-En Hsu, Angelo Anacleto, Jingyue Xi, Myungjin Kim, Ellen Pedersen, Olivia I Koues, Thomas Wilson, Changhee Lee, Goo Jun, Hyun Min Kang, Jun Hee Lee

Faculty, Staff and Student Publications

Spatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments. Seq-Scope repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, overcoming these limitations. It offers submicrometer resolution, high capture efficiency, rapid turnaround time and precise annotation of histopathology at a much lower cost than commercial alternatives. This protocol details the implementation of Seq-Scope with an Illumina NovaSeq 6000 …

Single-Cell Rna Sequencing Identifies Molecular Biomarkers Predicting Late Progression To Cdk4/6 Inhibition In Patients With Hr+/Her2- Metastatic Breast Cancer, Linjie Luo, Peng Yang, Sofia Mastoraki, Xiayu Rao, Yan Wang, Nicole M Kettner, Akshara Singareeka Raghavendra, Debasish Tripathy, Senthil Damodaran, Kelly K Hunt, Jing Wang, Ziyi Li, Khandan Keyomarsi

Faculty, Staff and Student Publications

Background: Cyclin-dependent kinase 4/6 inhibitors (CDK4/6is) in combination with endocrine therapy are the standard treatment for patients with hormone receptor-positive, HER2-negative metastatic breast cancer (mBC). Despite the efficacy of CDK4/6is, intrinsic resistance occurs in approximately one-third of patients, highlighting the need for reliable predictive biomarkers.

Methods: Single-cell RNA sequencing analyzed metastatic tumors from HR+/HER2- mBC patients pre-CDK4/6i treatment at baseline (BL) and/or at disease progression. BL samples were from CDK4/6i responders (median progression-free survival [mPFS] = 25.5 months), while progressors were categorized as early-progressors (EP, mPFS = 3 months) and late-progressors (LP, mPFS = 11 months). Metastatic sites included liver, …

A Deconvolution Framework That Uses Single-Cell Sequencing Plus A Small Benchmark Data Set For Accurate Analysis Of Cell Type Ratios In Complex Tissue Samples, Shuai Guo, Xiaoqian Liu, Xuesen Cheng, Yujie Jiang, Shuangxi Ji, Qingnan Liang, Andrew Koval, Yumei Li, Leah A Owen, Ivana K Kim, Ana Aparicio, Sanghoon Lee, Anil K Sood, Scott Kopetz, John Paul Shen, John N Weinstein, Margaret M Deangelis, Rui Chen, Wenyi Wang

Faculty, Staff and Student Publications

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we utilize an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using this well-matched, that is, benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using two benchmark data sets of healthy retinas and …

A Regularized Bayesian Dirichlet-Multinomial Regression Model For Integrating Single-Cell-Level Omics And Patient-Level Clinical Study Data, Yanghong Guo, Lei Yu, Lei Guo, Lin Xu, Qiwei Li

Faculty, Staff and Student Publications

The abundance of various cell types can vary significantly among patients with varying phenotypes and even those with the same phenotype. Recent scientific advancements provide mounting evidence that other clinical variables, such as age, gender, and lifestyle habits, can also influence the abundance of certain cell types. However, current methods for integrating single-cell-level omics data with clinical variables are inadequate. In this study, we propose a regularized Bayesian Dirichlet-multinomial regression framework to investigate the relationship between single-cell RNA sequencing data and patient-level clinical data. Additionally, the model employs a novel hierarchical tree structure to identify such relationships at different cell-type …

A Statistical Approach For Systematic Identification Of Transition Cells From Scrna-Seq Data, Yuanxin Wang, Merve Dede, Vakul Mohanty, Jinzhuang Dou, Ziyi Li, Ken Chen

Faculty, Staff and Student Publications

Decoding cellular state transitions is crucial for understanding complex biological processes in development and disease. While recent advancements in single-cell RNA sequencing (scRNA-seq) offer insights into cellular trajectories, existing tools primarily study expressional rather than regulatory state shifts. We present CellTran, a statistical approach utilizing paired-gene expression correlations to detect transition cells from scRNA-seq data without explicitly resolving gene regulatory networks. Applying our approach to various contexts, including tissue regeneration, embryonic development, preinvasive lesions, and humoral responses post-vaccination, reveals transition cells and their distinct gene expression profiles. Our study sheds light on the underlying molecular mechanisms driving cellular state transitions, …

Blinatumomab Maintenance After Allogeneic Hematopoietic Cell Transplantation For B-Lineage Acute Lymphoblastic Leukemia, Yuanxin Wang, Merve Dede, Vakul Mohanty, Jinzhuang Dou, Ziyi Li, Ken Chen

Faculty, Staff and Student Publications

Decoding cellular state transitions is crucial for understanding complex biological processes in development and disease. While recent advancements in single-cell RNA sequencing (scRNA-seq) offer insights into cellular trajectories, existing tools primarily study expressional rather than regulatory state shifts. We present CellTran, a statistical approach utilizing paired-gene expression correlations to detect transition cells from scRNA-seq data without explicitly resolving gene regulatory networks. Applying our approach to various contexts, including tissue regeneration, embryonic development, preinvasive lesions, and humoral responses post-vaccination, reveals transition cells and their distinct gene expression profiles. Our study sheds light on the underlying molecular mechanisms driving cellular state transitions, …

Accurate Identification Of Locally Aneuploid Cells By Incorporating Cytogenetic Information In Single Cell Data Analysis, Ziyi Li, Ruoxing Li, Irene Ganan-Gomez, Hussein A Abbas, Guillermo Garcia-Manero, Wei Sun

Faculty, Staff and Student Publications

Single-cell RNA sequencing is a powerful tool to investigate the cellular makeup of tumor samples. However, due to the sparse data and the complex tumor microenvironment, it can be challenging to identify neoplastic cells that play important roles in tumor growth and disease progression. This is especially relevant for blood cancers, where neoplastic cells may be highly similar to normal cells. To address this challenge, we have developed partCNV and partCNVH, two methods for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. PartCNV uses an expectation-maximization (EM) algorithm with mixtures of Poisson distributions and …

Scintruler: Guiding The Integration Of Multiple Single-Cell Rna-Seq Datasets With A Novel Statistical Metric, Yue Lyu, Steven H Lin, Hao Wu, Ziyi Li

Faculty, Staff and Student Publications

Motivation: The growing number of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets, such as augmenting sample sizes and enhancing analytical robustness. Inherent diversity and batch discrepancies within samples or across studies continue to pose significant challenges for computational analyses. Questions persist in practice, lacking definitive answers: Should we use a specific integration method or opt for simply merging the datasets during joint analysis? Among all the existing data integration methods, which one is more suitable in specific scenarios?

Result: To fill the gap, we introduce SCIntRuler, a novel statistical metric for guiding the integration of …

A Cell Cycle-Aware Network For Data Integration And Label Transferring Of Single-Cell Rna-Seq And Atac-Seq, Jiajia Liu, Jian Ma, Jianguo Wen, Xiaobo Zhou

Faculty, Staff and Student Publications

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell …

Maackia Amurensis Seed Lectin (Masl) And Soluble Human Podoplanin (Shpdpn) Sequence Analysis And Effects On Human Oral Squamous Cell Carcinoma (Oscc) Cell Migration And Viability, Ariel C Yin, Cayla J Holdcraft, Eamonn J Brace, Tyler J Hellmig, Sayan Basu, Saumil Parikh, Katarzyna Jachimowska, Evelyne Kalyoussef, Dylan Roden, Soly Baredes, Eugenio M Capitle, David I Suster, Alan J Shienbaum, Caifeng Zhao, Haiyan Zheng, Kevin Balcaen, Simon Devos, Jurgen Haustraete, Mahnaz Fatahzadeh, Gary S Goldberg

Rowan-Virtua School of Osteopathic Medicine Departmental Research

Maackia amurensis lectins serve as research and botanical agents that bind to sialic residues on proteins. For example, M. amurensis seed lectin (MASL) targets the sialic acid modified podoplanin (PDPN) receptor to suppress arthritic chondrocyte inflammation, and inhibit tumor cell growth and motility. However, M. amurensis lectin nomenclature and composition are not clearly defined. Here, we sought to definitively characterize MASL and its effects on tumor cell behavior. We utilized SDS-PAGE and LC-MS/MS to find that M. amurensis lectins can be divided into two groups. MASL is a member of one group which is composed of subunits that form dimers, …

Evaluating Performance And Applications Of Sample-Wise Cell Deconvolution Methods On Human Brain Transcriptomic Data, Rujia Dai, Tianyao Chu, Ming Zhang, Xuan Wang, Alexandre Jourdon, Feinan Wu, Jessica Mariani, Flora M Vaccarino, Donghoon Lee, John F Fullard, Gabriel E Hoffman, Panos Roussos, Yue Wang, Xusheng Wang, Dalila Pinto, Sidney H Wang, Chunling Zhang, Psychencode Consortium, Chao Chen, Chunyu Liu

Faculty, Staff and Student Publications

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The …

Casi: A Framework For Cross-Timepoint Analysis Of Single-Cell Rna Sequencing Data, Yizhuo Wang, Christopher R Flowers, Michael Wang, Xuelin Huang, Ziyi Li

Faculty, Staff and Student Publications

Single-cell RNA sequencing (scRNA-seq) technology has been widely used to study the differences in gene expression at the single cell level, providing insights into the research of cell development, differentiation, and functional heterogeneity. Various pipelines and workflows of scRNA-seq analysis have been developed but few considered multi-timepoint data specifically. In this study, we develop CASi, a comprehensive framework for analyzing multiple timepoints' scRNA-seq data, which provides users with: (1) cross-timepoint cell annotation, (2) detection of potentially novel cell types emerged over time, (3) visualization of cell population evolution, and (4) identification of temporal differentially expressed genes (tDEGs). Through comprehensive simulation …

Data Normalization For Addressing The Challenges In The Analysis Of Single-Cell Transcriptomic Datasets, Raquel Cuevas-Diaz Duran, Haichao Wei, Jiaqian Wu

Faculty, Staff and Student Publications

BACKGROUND: Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data.

MAIN BODY: The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most …

Identification Of Colorectal Cancer Cell Stemness From Single-Cell Rna Sequencing, Kangyu Lin, Saikat Chowdhury, Mohammad A Zeineddine, Fadl A Zeineddine, Nicholas J Hornstein, Oscar E Villarreal, Dipen M Maru, Cara L Haymaker, Jean-Nicolas Vauthey, George J Chang, Elena Bogatenkova, David Menter, Scott Kopetz, John Paul Shen

Faculty, Staff and Student Publications

UNLABELLED: Cancer stem cells (CSC) play a critical role in metastasis, relapse, and therapy resistance in colorectal cancer. While characterization of the normal lineage of cell development in the intestine has led to the identification of many genes involved in the induction and maintenance of pluripotency, recent studies suggest significant heterogeneity in CSC populations. Moreover, while many canonical colorectal cancer CSC marker genes have been identified, the ability to use these classical markers to annotate stemness at the single-cell level is limited. In this study, we performed single-cell RNA sequencing on a cohort of 6 primary colon, 9 liver metastatic …

Transcription-Replication Interactions Reveal Bacterial Genome Regulation, Andrew W Pountain, Peien Jiang, Tianyou Yao, Ehsan Homaee, Yichao Guan, Kevin J C Mcdonald, Magdalena Podkowik, Bo Shopsin, Victor J Torres, Ido Golding, Itai Yanai

Faculty, Staff and Student Publications

Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome1. In bacteria, the relationship between the regulatory architecture of a gene and its expression is well understood for individual model gene circuits2,3. However, a broader perspective of these dynamics at the genome scale is lacking, in part because bacterial transcriptomics has hitherto captured only a static snapshot of expression averaged across millions of cells4. As a result, the full diversity of gene expression dynamics and their relation to regulatory architecture remains unknown. Here we present a novel genome-wide classification of …

A Relay Velocity Model Infers Cell-Dependent Rna Velocity, Shengyu Li, Pengzhi Zhang, Weiqing Chen, Lingqun Ye, Kristopher W Brannan, Nhat-Tu Le, Jun-Ichi Abe, John P Cooke, Guangyu Wang

Faculty, Staff and Student Publications

RNA velocity provides an approach for inferring cellular state transitions from single-cell RNA sequencing (scRNA-seq) data. Conventional RNA velocity models infer universal kinetics from all cells in an scRNA-seq experiment, resulting in unpredictable performance in experiments with multi-stage and/or multi-lineage transition of cell states where the assumption of the same kinetic rates for all cells no longer holds. Here we present cellDancer, a scalable deep neural network that locally infers velocity for each cell from its neighbors and then relays a series of local velocities to provide single-cell resolution inference of velocity kinetics. In the simulation benchmark, cellDancer shows robust …

Self-Supervised Deep Clustering Of Single-Cell Rna-Seq Data To Hierarchically Detect Rare Cell Populations., Tianyuan Lei, Ruoyu Chen, Shaoqiang Zhang, Yong Chen

College of Science & Mathematics Departmental Research

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. …

Compass: Joint Copy Number And Mutation Phylogeny Reconstruction From Amplicon Single-Cell Sequencing Data, Etienne Sollier, Jack Kuipers, Koichi Takahashi, Niko Beerenwinkel, Katharina Jahn

Faculty, Staff and Student Publications

Reconstructing the history of somatic DNA alterations can help understand the evolution of a tumor and predict its resistance to treatment. Single-cell DNA sequencing (scDNAseq) can be used to investigate clonal heterogeneity and to inform phylogeny reconstruction. However, most existing phylogenetic methods for scDNAseq data are designed either for single nucleotide variants (SNVs) or for large copy number alterations (CNAs), or are not applicable to targeted sequencing. Here, we develop COMPASS, a computational method for inferring the joint phylogeny of SNVs and CNAs from targeted scDNAseq data. We evaluate COMPASS on simulated data and apply it to several datasets including …

Characterizing Cancer Metabolism From Bulk And Single-Cell Rna-Seq Data Using Metaflux, Yuefan Huang, Vakul Mohanty, Merve Dede, Kyle Tsai, May Daher, Li Li, Katayoun Rezvani, Ken Chen

Faculty, Staff and Student Publications

Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) …

Mitotrace: A Computational Framework For Analyzing Mitochondrial Variation In Single-Cell Rna Sequencing Data, Mingqiang Wang, Wankun Deng, David C Samuels, Zhongming Zhao, Lukas M Simon

Faculty, Staff and Student Publications

Genetic variation in the mitochondrial genome is linked to important biological functions and various human diseases. Recent progress in single-cell genomics has established single-cell RNA sequencing (scRNAseq) as a popular and powerful technique to profile transcriptomics at the cellular level. While most studies focus on deciphering gene expression, polymorphisms including mitochondrial variants can also be readily inferred from scRNAseq. However, limited attention has been paid to investigate the single-cell landscape of mitochondrial variants, despite the rapid accumulation of scRNAseq data in the community. In addition, a diploid context is assumed for most variant calling tools, which is not appropriate for …

Deep Single-Cell Rna-Seq Data Clustering With Graph Prototypical Contrastive Learning, Junseok Lee, Sungwon Kim, Dongmin Hyun, Namkyeong Lee, Yejin Kim, Chanyoung Park

Faculty, Staff and Student Publications

MOTIVATION: Single-cell RNA sequencing enables researchers to study cellular heterogeneity at single-cell level. To this end, identifying cell types of cells with clustering techniques becomes an important task for downstream analysis. However, challenges of scRNA-seq data such as pervasive dropout phenomena hinder obtaining robust clustering outputs. Although existing studies try to alleviate these problems, they fall short of fully leveraging the relationship information and mainly rely on reconstruction-based losses that highly depend on the data quality, which is sometimes noisy.

RESULTS: This work proposes a graph-based prototypical contrastive learning method, named scGPCL. Specifically, scGPCL encodes the cell representations using Graph …