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Worley J, Noh H, You D, Turunen MM, Ding H, Paull E, Griffin AT, Grunn A, Zhang M, Guillan K, Bush EC, Brosius SJ, Hibshoosh H, Mundi PS, Sims P, Dalerba P, Dela Cruz FS, Kung AL, Califano A. Identification and Pharmacological Targeting of Treatment-Resistant, Stem-like Breast Cancer Cells for Combination Therapy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.08.562798. [PMID: 38798673 PMCID: PMC11118419 DOI: 10.1101/2023.11.08.562798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Tumors frequently harbor isogenic yet epigenetically distinct subpopulations of multi-potent cells with high tumor-initiating potential-often called Cancer Stem-Like Cells (CSLCs). These can display preferential resistance to standard-of-care chemotherapy. Single-cell analyses can help elucidate Master Regulator (MR) proteins responsible for governing the transcriptional state of these cells, thus revealing complementary dependencies that may be leveraged via combination therapy. Interrogation of single-cell RNA sequencing profiles from seven metastatic breast cancer patients, using perturbational profiles of clinically relevant drugs, identified drugs predicted to invert the activity of MR proteins governing the transcriptional state of chemoresistant CSLCs, which were then validated by CROP-seq assays. The top drug, the anthelmintic albendazole, depleted this subpopulation in vivo without noticeable cytotoxicity. Moreover, sequential cycles of albendazole and paclitaxel-a commonly used chemotherapeutic -displayed significant synergy in a patient-derived xenograft (PDX) from a TNBC patient, suggesting that network-based approaches can help develop mechanism-based combinatorial therapies targeting complementary subpopulations.
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Affiliation(s)
- Jeremy Worley
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY USA 10032
| | - Heeju Noh
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Daoqi You
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Mikko M Turunen
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Hongxu Ding
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Pharmacy Practice & Science, College of Pharmacy, University of Arizona, Tucson, Arizona, USA 85721
| | - Evan Paull
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Aaron T Griffin
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Adina Grunn
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Mingxuan Zhang
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Kristina Guillan
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Erin C Bush
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Samantha J Brosius
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Hanina Hibshoosh
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, USA 10032
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, USA 10032
| | - Prabhjot S Mundi
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, USA 10032
| | - Peter Sims
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Piero Dalerba
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, USA 10032
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, USA 10032
- Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, USA 10032
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
| | - Filemon S Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrew L Kung
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrea Califano
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, USA 10032
- Department of Biochemistry & Molecular Biophysics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY USA 10032
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2
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Popp JM, Rhodes K, Jangi R, Li M, Barr K, Tayeb K, Battle A, Gilad Y. Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592174. [PMID: 38746382 PMCID: PMC11092595 DOI: 10.1101/2024.05.02.592174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell-types and developmental stages remain underexplored. Here we harnessed the potential of heterogeneous differentiating cultures ( HDCs ), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell-types. We generated HDCs for 53 human donors and collected single-cell RNA-sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell-types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues, and dynamic regulatory effects associated with a range of complex traits.
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3
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Qadir MMF, Elgamal RM, Song K, Kudtarkar P, Sakamuri SS, Katakam PV, El-Dahr S, Kolls J, Gaulton KJ, Mauvais-Jarvis F. Single cell regulatory architecture of human pancreatic islets suggests sex differences in β cell function and the pathogenesis of type 2 diabetes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.11.589096. [PMID: 38645001 PMCID: PMC11030320 DOI: 10.1101/2024.04.11.589096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Biological sex affects the pathogenesis of type 2 and type 1 diabetes (T2D, T1D) including the development of β cell failure observed more often in males. The mechanisms that drive sex differences in β cell failure is unknown. Studying sex differences in islet regulation and function represent a unique avenue to understand the sex-specific heterogeneity in β cell failure in diabetes. Here, we examined sex and race differences in human pancreatic islets from up to 52 donors with and without T2D (including 37 donors from the Human Pancreas Analysis Program [HPAP] dataset) using an orthogonal series of experiments including single cell RNA-seq (scRNA-seq), single nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq), dynamic hormone secretion, and bioenergetics. In cultured islets from nondiabetic (ND) donors, in the absence of the in vivo hormonal environment, sex differences in islet cell type gene accessibility and expression predominantly involved sex chromosomes. Of particular interest were sex differences in the X-linked KDM6A and Y-linked KDM5D chromatin remodelers in female and male islet cells respectively. Islets from T2D donors exhibited similar sex differences in differentially expressed genes (DEGs) from sex chromosomes. However, in contrast to islets from ND donors, islets from T2D donors exhibited major sex differences in DEGs from autosomes. Comparing β cells from T2D and ND donors revealed that females had more DEGs from autosomes compared to male β cells. Gene set enrichment analysis of female β cell DEGs showed a suppression of oxidative phosphorylation and electron transport chain pathways, while male β cell had suppressed insulin secretion pathways. Thus, although sex-specific differences in gene accessibility and expression of cultured ND human islets predominantly affect sex chromosome genes, major differences in autosomal gene expression between sexes appear during the transition to T2D and which highlight mitochondrial failure in female β cells.
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Affiliation(s)
- Mirza Muhammad Fahd Qadir
- Section of Endocrinology and Metabolism, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
- Southeast Louisiana Veterans Health Care System, New Orleans, LA, USA
- Tulane Center of Excellence in Sex-Based Biology & Medicine, New Orleans, LA, USA
| | - Ruth M. Elgamal
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Keijing Song
- Center for Translational Research in Infection and Inflammation, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Siva S.V.P Sakamuri
- Department of Pharmacology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Prasad V. Katakam
- Department of Pharmacology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Samir El-Dahr
- Department of Pediatrics, Tulane University, School of Medicine, New Orleans, LA, USA
| | - Jay Kolls
- Center for Translational Research in Infection and Inflammation, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Franck Mauvais-Jarvis
- Section of Endocrinology and Metabolism, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
- Southeast Louisiana Veterans Health Care System, New Orleans, LA, USA
- Tulane Center of Excellence in Sex-Based Biology & Medicine, New Orleans, LA, USA
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Qian ZY, Pan YQ, Li XX, Chen YX, Wu HX, Liu ZX, Kosar M, Bartek J, Wang ZX, Xu RH. Modulator of TMB-associated immune infiltration (MOTIF) predicts immunotherapy response and guides combination therapy. Sci Bull (Beijing) 2024; 69:803-822. [PMID: 38320897 DOI: 10.1016/j.scib.2024.01.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/04/2023] [Accepted: 12/07/2023] [Indexed: 02/08/2024]
Abstract
Patients with high tumor mutational burden (TMB) levels do not consistently respond to immune checkpoint inhibitors (ICIs), possibly because a high TMB level does not necessarily result in adequate infiltration of CD8+ T cells. Using bulk ribonucleic acid sequencing (RNA-seq) data from 9311 tumor samples across 30 cancer types, we developed a novel tool called the modulator of TMB-associated immune infiltration (MOTIF), which comprises genes that can determine the extent of CD8+ T cell infiltration prompted by a certain TMB level. We confirmed that MOTIF can accurately reflect the integrity and defects of the cancer-immunity cycle. By analyzing 84 human single-cell RNA-seq datasets from 32 types of solid tumors, we revealed that MOTIF can provide insights into the diverse roles of various cell types in the modulation of CD8+ T cell infiltration. Using pretreatment RNA-seq data from 13 ICI-treated cohorts, we validated the use of MOTIF in predicting CD8+ T cell infiltration and ICI efficacy. Among the components of MOTIF, we identified EMC3 as a negative regulator of CD8+ T cell infiltration, which was validated via in vivo studies. Additionally, MOTIF provided guidance for the potential combinations of programmed death 1 blockade with certain immunostimulatory drugs to facilitate CD8+ T cell infiltration and improve ICI efficacy.
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Affiliation(s)
- Zheng-Yu Qian
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China
| | - Yi-Qian Pan
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China
| | - Xue-Xin Li
- Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm S-171 21, Sweden; Department of General Surgery, The Fourth Affiliated Hospital, China Medical University, Shenyang 110032, China
| | - Yan-Xing Chen
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China
| | - Hao-Xiang Wu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China
| | - Ze-Xian Liu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China; Bioinformatics Platform, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Laboratory of Artificial Intelligence and Data Science, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Martin Kosar
- Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm S-171 21, Sweden; Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Haining 314400, China; Edinburgh Medical School, Biomedical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh EH1 1LT, UK
| | - Jiri Bartek
- Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm S-171 21, Sweden; Danish Cancer Society Research Center, Copenhagen DK-2100, Denmark.
| | - Zi-Xian Wang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China; Laboratory of Artificial Intelligence and Data Science, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
| | - Rui-Hua Xu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China; Laboratory of Artificial Intelligence and Data Science, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
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5
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van der Heide V, Davenport B, Cubitt B, Roudko V, Choo D, Humblin E, Jhun K, Angeliadis K, Dawson T, Furtado G, Kamphorst A, Ahmed R, de la Torre JC, Homann D. Functional impairment of "helpless" CD8 + memory T cells is transient and driven by prolonged but finite cognate antigen presentation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.22.576725. [PMID: 38328184 PMCID: PMC10849538 DOI: 10.1101/2024.01.22.576725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Generation of functional CD8 + T cell memory typically requires engagement of CD4 + T cells. However, in certain scenarios, such as acutely-resolving viral infections, effector (T E ) and subsequent memory (T M ) CD8 + T cell formation appear impervious to a lack of CD4 + T cell help during priming. Nonetheless, such "helpless" CD8 + T M respond poorly to pathogen rechallenge. At present, the origin and long-term evolution of helpless CD8 + T cell memory remain incompletely understood. Here, we demonstrate that helpless CD8 + T E differentiation is largely normal but a multiplicity of helpless CD8 T M defects, consistent with impaired memory maturation, emerge as a consequence of prolonged yet finite exposure to cognate antigen. Importantly, these defects resolve over time leading to full restoration of CD8 + T M potential and recall capacity. Our findings provide a unified explanation for helpless CD8 + T cell memory and emphasize an unexpected CD8 + T M plasticity with implications for vaccination strategies and beyond.
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6
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Gilis J, Perin L, Malfait M, Van den Berge K, Takele Assefa A, Verbist B, Risso D, Clement L. Differential detection workflows for multi-sample single-cell RNA-seq data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.17.572043. [PMID: 38187695 PMCID: PMC10769270 DOI: 10.1101/2023.12.17.572043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.
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Affiliation(s)
- Jeroen Gilis
- These authors contributed equally
- Applied Mathematics, Computer science and Statistics, Ghent University, Ghent, 9000, Belgium
- Bioinformatics Institute, Ghent University, Ghent, 9000, Belgium
- Data Mining and Modeling for Biomedicine, VIB Flemish Institute for Biotechnology, Ghent, 9000, Belgium
| | - Laura Perin
- These authors contributed equally
- Department of Statistical Sciences, University of Padova, Padova, Italy
| | - Milan Malfait
- Applied Mathematics, Computer science and Statistics, Ghent University, Ghent, 9000, Belgium
| | - Koen Van den Berge
- Statistics and Decision Sciences, Johnson and Johnson Innovative Medicine, Beerse, Belgium
| | - Alemu Takele Assefa
- Statistics and Decision Sciences, Johnson and Johnson Innovative Medicine, Beerse, Belgium
| | - Bie Verbist
- Statistics and Decision Sciences, Johnson and Johnson Innovative Medicine, Beerse, Belgium
| | - Davide Risso
- Department of Statistical Sciences, University of Padova, Padova, Italy
- Padua Center for Network Medicine, University of Padova, Padova, Italy
| | - Lieven Clement
- Applied Mathematics, Computer science and Statistics, Ghent University, Ghent, 9000, Belgium
- Bioinformatics Institute, Ghent University, Ghent, 9000, Belgium
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7
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Murphy AE, Fancy N, Skene N. Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer's disease dataset. eLife 2023; 12:RP90214. [PMID: 38047913 PMCID: PMC10695556 DOI: 10.7554/elife.90214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023] Open
Abstract
Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer's disease (AD) (Mathys et al., 2019). With bulk RNA-seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-seq differential expression, resulting in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes.
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Affiliation(s)
- Alan E Murphy
- UK Dementia Research Institute at Imperial College LondonLondonUnited Kingdom
- Department of Brain Sciences, Imperial College LondonLondonUnited Kingdom
| | - Nurun Fancy
- UK Dementia Research Institute at Imperial College LondonLondonUnited Kingdom
- Department of Brain Sciences, Imperial College LondonLondonUnited Kingdom
| | - Nathan Skene
- UK Dementia Research Institute at Imperial College LondonLondonUnited Kingdom
- Department of Brain Sciences, Imperial College LondonLondonUnited Kingdom
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8
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Wang H, Lin YN, Yan S, Hong JP, Tan JR, Chen YQ, Cao YS, Fang W. NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning. PLANT METHODS 2023; 19:119. [PMID: 37925413 PMCID: PMC10625708 DOI: 10.1186/s13007-023-01092-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 10/15/2023] [Indexed: 11/06/2023]
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying the cellular heterogeneity of rice roots. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity. RESULTS To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using a test dataset, with 98.01% accuracy and 95.45% recall. With the power of interpretability provided by NRTPredictor, our model recognizes 110 marker genes partially involved in phenylpropanoid biosynthesis. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrated analysis of scRNA and bulk RNA-seq data revealed aberrant expression of Epidermis cell subpopulations in flooding, Pi, and salt stresses. CONCLUSION Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi, and salt stresses. Based on the proposed model, a free webserver has been established, which is available at https://www.cgris.net/nrtp .
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Affiliation(s)
- Hao Wang
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yu-Nan Lin
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Shen Yan
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jing-Peng Hong
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jia-Rui Tan
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yan-Qing Chen
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Yong-Sheng Cao
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Wei Fang
- The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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9
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Meyers S, Demeyer S, Cools J. CRISPR screening in hematology research: from bulk to single-cell level. J Hematol Oncol 2023; 16:107. [PMID: 37875911 PMCID: PMC10594891 DOI: 10.1186/s13045-023-01495-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/21/2023] [Indexed: 10/26/2023] Open
Abstract
The CRISPR genome editing technology has revolutionized the way gene function is studied. Genome editing can be achieved in single genes or for thousands of genes simultaneously in sensitive genetic screens. While conventional genetic screens are limited to bulk measurements of cell behavior, recent developments in single-cell technologies make it possible to combine CRISPR screening with single-cell profiling. In this way, cell behavior and gene expression can be monitored simultaneously, with the additional possibility of including data on chromatin accessibility and protein levels. Moreover, the availability of various Cas proteins leading to inactivation, activation, or other effects on gene function further broadens the scope of such screens. The integration of single-cell multi-omics approaches with CRISPR screening open the path to high-content information on the impact of genetic perturbations at single-cell resolution. Current limitations in cell throughput and data density need to be taken into consideration, but new technologies are rapidly evolving and are likely to easily overcome these limitations. In this review, we discuss the use of bulk CRISPR screening in hematology research, as well as the emergence of single-cell CRISPR screening and its added value to the field.
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Affiliation(s)
- Sarah Meyers
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Sofie Demeyer
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Jan Cools
- Center for Human Genetics, KU Leuven, Leuven, Belgium.
- Center for Cancer Biology, VIB, Leuven, Belgium.
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium.
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10
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Foody GM. Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient. PLoS One 2023; 18:e0291908. [PMID: 37792898 PMCID: PMC10550141 DOI: 10.1371/journal.pone.0291908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 09/07/2023] [Indexed: 10/06/2023] Open
Abstract
The accuracy of a classification is fundamental to its interpretation, use and ultimately decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the true accuracy. Mis-estimation of classification accuracy metrics and associated mis-interpretations are often due to variations in prevalence and the use of an imperfect reference standard. The fundamental issues underlying the problems associated with variations in prevalence and reference standard quality are revisited here for binary classifications with particular attention focused on the use of the Matthews correlation coefficient (MCC). A key attribute claimed of the MCC is that a high value can only be attained when the classification performed well on both classes in a binary classification. However, it is shown here that the apparent magnitude of a set of popular accuracy metrics used in fields such as computer science medicine and environmental science (Recall, Precision, Specificity, Negative Predictive Value, J, F1, likelihood ratios and MCC) and one key attribute (prevalence) were all influenced greatly by variations in prevalence and use of an imperfect reference standard. Simulations using realistic values for data quality in applications such as remote sensing showed each metric varied over the range of possible prevalence and at differing levels of reference standard quality. The direction and magnitude of accuracy metric mis-estimation were a function of prevalence and the size and nature of the imperfections in the reference standard. It was evident that the apparent MCC could be substantially under- or over-estimated. Additionally, a high apparent MCC arose from an unquestionably poor classification. As with some other metrics of accuracy, the utility of the MCC may be overstated and apparent values need to be interpreted with caution. Apparent accuracy and prevalence values can be mis-leading and calls for the issues to be recognised and addressed should be heeded.
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Affiliation(s)
- Giles M. Foody
- School of Geography, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom
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11
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Han G, Yan D, Sun Z, Fang J, chang X, Wilson L, Liu Y. Bayesian-frequentist hybrid inference framework for single cell RNA-seq analyses. RESEARCH SQUARE 2023:rs.3.rs-3384541. [PMID: 37886581 PMCID: PMC10602069 DOI: 10.21203/rs.3.rs-3384541/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Background Single cell RNA sequencing technology (scRNA-seq) has been proven useful in understanding cell-specific disease mechanisms. However, identifying genes of interest remains a key challenge. Pseudo-bulk methods that pool scRNA-seq counts in the same biological replicates have been commonly used to identify differentially expressed genes. However, such methods may lack power due to the limited sample size of scRNA-seq datasets, which can be prohibitively expensive. Results Motivated by this, we proposed to use the Bayesian-frequentist hybrid (BFH) framework to increase the power. Conclusion In our idiopathic pulmonary fibrosis (IPF) case study, we demonstrated that with a proper informative prior, the BFH approach identified more genes of interest. Furthermore, these genes were reasonable based on the current knowledge of IPF. Thus, the BFH offers a unique and flexible framework for future scRNA-seq analyses.
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Affiliation(s)
- Gang Han
- Epidemiology & Biostatistics, 212 Adriance Lab Rd, 1266 TAMU College Station, TX 77843
| | - Dongyan Yan
- Eli Lilly and Company Corporate Center, 893 Delaware St, Indianapolis, IN 46225
| | - Zhe Sun
- Eli Lilly and Company Corporate Center, 893 Delaware St, Indianapolis, IN 46225
| | - Jiyuan Fang
- Eli Lilly and Company Corporate Center, 893 Delaware St, Indianapolis, IN 46225
| | - Xinyue chang
- Eli Lilly and Company Corporate Center, 893 Delaware St, Indianapolis, IN 46225
| | - Lucas Wilson
- Epidemiology & Biostatistics, 212 Adriance Lab Rd, 1266 TAMU College Station, TX 77843
| | - Yushi Liu
- Eli Lilly and Company Corporate Center, 893 Delaware St, Indianapolis, IN 46225
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12
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Kim J, Lee J, Li X, Kunjravia N, Rambhia D, Cueto I, Kim K, Chaparala V, Ko Y, Garcet S, Zhou W, Cao J, Krueger JG. Multi-omics segregate different transcriptomic impacts of anti-IL-17A blockade on type 17 T-cells and regulatory immune cells in psoriasis skin. Front Immunol 2023; 14:1250504. [PMID: 37781383 PMCID: PMC10536146 DOI: 10.3389/fimmu.2023.1250504] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
Durable psoriasis improvement has been reported in a subset of psoriasis patients after treatment withdrawal of biologics blocking IL-23/Type 17 T-cell (T17) autoimmune axis. However, it is not well understood if systemic blockade of the IL-23/T17 axis promotes immune tolerance in psoriasis skin. The purpose of the study was to find translational evidence that systemic IL-17A blockade promotes regulatory transcriptome modification in human psoriasis skin immune cell subsets. We analyzed human psoriasis lesional skin 6 mm punch biopsy tissues before and after systemic IL-17A blockade using the muti-genomics approach integrating immune cell-enriched scRNA-seq (n = 18), microarray (n = 61), and immunohistochemistry (n = 61) with repository normal control skin immune cell-enriched scRNA-seq (n = 10) and microarray (n = 8) data. For the T17 axis transcriptome, systemic IL-17A blockade depleted 100% of IL17A + T-cells and 95% of IL17F + T-cells in psoriasis skin. The expression of IL23A in DC subsets was also downregulated by IL-17A blockade. The expression of IL-17-driven inflammatory mediators (IL36G, S100A8, DEFB4A, and DEFB4B) in suprabasal keratinocytes was correlated with psoriasis severity and was downregulated by IL-17A blockade. For the regulatory DC transcriptome, the proportion of regulatory semimature DCs expressing regulatory DC markers of BDCA-3 (THBD) and DCIR (CLEC4A) was increased in posttreatment psoriasis lesional skin compared to pretreatment psoriasis lesional skin. In addition, IL-17A blockade induced higher expression of CD1C and CD14, which are markers of CD1c+ CD14+ dendritic cell (DC) subset that suppresses antigen-specific T-cell responses, in posttreatment regulatory semimature DCs compared to pretreatment regulatory semimature DCs. In conclusion, systemic IL-17A inhibition not only blocks the entire IL-23/T17 cell axis but also promotes regulatory gene expression in regulatory DCs in human psoriasis skin.
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Affiliation(s)
- Jaehwan Kim
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
- Dermatology Section, Veterans Affairs Northern California Health Care System, Mather, CA, United States
- Department of Dermatology, University of California, Davis, Sacramento, CA, United States
| | - Jongmi Lee
- Dermatology Section, Veterans Affairs Northern California Health Care System, Mather, CA, United States
| | - Xuan Li
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
| | - Norma Kunjravia
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
| | - Darshna Rambhia
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
| | - Inna Cueto
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
| | - Katherine Kim
- Dermatology Section, Veterans Affairs Northern California Health Care System, Mather, CA, United States
- Department of Dermatology, University of California, Davis, Sacramento, CA, United States
| | - Vasuma Chaparala
- Dermatology Section, Veterans Affairs Northern California Health Care System, Mather, CA, United States
| | - Younhee Ko
- Department of Dermatology, University of California, Davis, Sacramento, CA, United States
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Seoul, Republic of Korea
| | - Sandra Garcet
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
- Research Bioinformatics, Center for Clinical and Translational Science, The Rockefeller University, New York, NY, United States
| | - Wei Zhou
- Laboratory of Single-cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, United States
| | - Junyue Cao
- Laboratory of Single-cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, United States
| | - James G. Krueger
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
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13
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You Y, Dong X, Wee YK, Maxwell MJ, Alhamdoosh M, Smyth GK, Hickey PF, Ritchie ME, Law CW. Modeling group heteroscedasticity in single-cell RNA-seq pseudo-bulk data. Genome Biol 2023; 24:107. [PMID: 37147723 PMCID: PMC10160736 DOI: 10.1186/s13059-023-02949-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 04/21/2023] [Indexed: 05/07/2023] Open
Abstract
Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and its presence can hamper the detection of differentially expressed genes. Since most bulk RNA-seq methods assume equal group variances, we introduce two new approaches that account for heteroscedastic groups, namely voomByGroup and voomWithQualityWeights using a blocked design (voomQWB). Compared to current gold-standard methods that do not account for group heteroscedasticity, we show results from simulations and various experiments that demonstrate the superior performance of voomByGroup and voomQWB in terms of error control and power when group variances in pseudo-bulk single-cell RNA-seq data are unequal.
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Affiliation(s)
- Yue You
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Australia.
- Department of Medical Biology, The University of Melbourne, Parkville, Australia.
| | | | | | | | | | - Gordon K Smyth
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Australia
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia
| | - Peter F Hickey
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
- Advanced Technology and Biology Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Australia
| | - Matthew E Ritchie
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Charity W Law
- Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
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14
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Gene function and cell surface protein association analysis based on single-cell multiomics data. Comput Biol Med 2023; 157:106733. [PMID: 36924730 DOI: 10.1016/j.compbiomed.2023.106733] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/08/2023] [Accepted: 02/28/2023] [Indexed: 03/05/2023]
Abstract
Single-cell transcriptomics provides researchers with a powerful tool to resolve the transcriptome heterogeneity of individual cells. However, this method falls short in revealing cellular heterogeneity at the protein level. Previous single-cell multiomics studies have focused on data integration rather than exploiting the full potential of multiomics data. Here we introduce a new analysis framework, gene function and protein association (GFPA), that mines reliable associations between gene function and cell surface protein from single-cell multimodal data. Applying GFPA to human peripheral blood mononuclear cells (PBMCs), we observe an association of epithelial mesenchymal transition (EMT) with the CD99 protein in CD4 T cells, which is consistent with previous findings. Our results show that GFPA is reliable across multiple cell subtypes and PBMC samples. The GFPA python packages and detailed tutorials are freely available at https://github.com/studentiz/GFPA.
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15
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Zimmerman KD, Evans C, Langefeld CD. Reply to: A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis. Nat Commun 2022; 13:7852. [PMID: 36550147 PMCID: PMC9780219 DOI: 10.1038/s41467-022-35520-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Kip D. Zimmerman
- grid.241167.70000 0001 2185 3318Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC USA
| | - Ciaran Evans
- grid.241167.70000 0001 2185 3318Department of Statistical Sciences, Wake Forest University, Winston-Salem, NC USA
| | - Carl D. Langefeld
- grid.241167.70000 0001 2185 3318Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC USA
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