1
|
Singh A, Khiabanian H. Feature selection followed by a novel residuals-based normalization that includes variance stabilization simplifies and improves single-cell gene expression analysis. BMC Bioinformatics 2024; 25:248. [PMID: 39080559 PMCID: PMC11290295 DOI: 10.1186/s12859-024-05872-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Accepted: 07/16/2024] [Indexed: 08/02/2024] Open
Abstract
Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are reduction of systematic biases primarily introduced through technical sources and transformation of counts to make them more amenable for the application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo.
Collapse
Affiliation(s)
- Amartya Singh
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA.
| | - Hossein Khiabanian
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA
- Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| |
Collapse
|
2
|
Yang J, Chen S, Ma F, Ding N, Mi S, Zhao Q, Xing Y, Yang T, Xing K, Yu Y, Wang C. Pathogen stimulations and immune cells synergistically affect the gene expression profile characteristics of porcine peripheral blood mononuclear cells. BMC Genomics 2024; 25:719. [PMID: 39054472 PMCID: PMC11270792 DOI: 10.1186/s12864-024-10603-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 07/08/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Pigs serve as a crucial source of protein in the human diet and play a fundamental role in ensuring food security. However, infectious diseases caused by bacteria or viruses are a major threat to effective global pig farming, jeopardizing human health. Peripheral blood mononuclear cells (PBMCs) are a mixture of immune cells that play crucial roles in immunity and disease resistance in pigs. Previous studies on the gene expression regulation patterns of PBMCs have concentrated on a single immune stimulus or immune cell subpopulation, which has limited our comprehensive understanding of the mechanisms of the pig immune response. RESULTS Here, we integrated and re-analyzed RNA-seq data published online for porcine PBMC stimulated by lipopolysaccharide (LPS), polyinosinic acid (PolyI:C), and various unknown microorganisms (EM). The results revealed that gene expression and its functional characterization are highly specific to the pathogen, identifying 603, 254, and 882 pathogen-specific genes and 38 shared genes, respectively. Notably, LPS and PolyI:C stimulation directly triggered inflammatory and immune-response pathways, while exposure to mixed microbes (EM) enhanced metabolic processes. These pathogen-specific genes were enriched in immune trait-associated quantitative trait loci (QTL) and eGenes in porcine immune tissues and were implicated in specific cell types. Furthermore, we discussed the roles of eQTLs rs3473322705 and rs1109431654 in regulating pathogen- and cell-specific genes CD300A and CD93, using cellular experiments. Additionally, by integrating genome-wide association studies datasets from 33 complex traits and diseases in humans, we found that pathogen-specific genes were significantly enriched for immune traits and metabolic diseases. CONCLUSIONS We systematically analyzed the gene expression profiles of the three stimulations and demonstrated pathogen-specific and cell-specific gene regulation across different stimulations in porcine PBMCs. These findings enhance our understanding of shared and distinct regulatory mechanisms of genetic variants in pig immune traits.
Collapse
Affiliation(s)
- Jinyan Yang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Siqian Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Fuping Ma
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Ning Ding
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Siyuan Mi
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Qingyao Zhao
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Yue Xing
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Ting Yang
- Dabei-Nong Science and Technology Group Co., Ltd, Beijing, 100080, China
| | - Kai Xing
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China
| | - Ying Yu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China.
| | - Chuduan Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technologyn, China Agricultural University, Beijing, 100193, China.
| |
Collapse
|
3
|
Vaidehi Narayanan H, Xiang MY, Chen Y, Huang H, Roy S, Makkar H, Hoffmann A, Roy K. Direct observation correlates NFκB cRel in B cells with activating and terminating their proliferative program. Proc Natl Acad Sci U S A 2024; 121:e2309686121. [PMID: 39024115 PMCID: PMC11287273 DOI: 10.1073/pnas.2309686121] [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] [Received: 07/19/2023] [Accepted: 05/28/2024] [Indexed: 07/20/2024] Open
Abstract
Antibody responses require the proliferative expansion of B cells controlled by affinity-dependent signals. Yet, proliferative bursts are heterogeneous, varying between 0 and 8 divisions in response to the same stimulus. NFκB cRel is activated in response to immune stimulation in B cells and is genetically required for proliferation. Here, we asked whether proliferative heterogeneity is controlled by natural variations in cRel abundance. We developed a fluorescent reporter mTFP1-cRel for the direct observation of cRel in live proliferating B cells. We found that cRel is heterogeneously distributed among naïve B cells, which are enriched for high expressors in a heavy-tailed distribution. We found that high cRel expressors show faster activation of the proliferative program, but do not sustain it well, with population expansion decaying earlier. With a mathematical model of the molecular network, we showed that cRel heterogeneity arises from balancing positive feedback by autoregulation and negative feedback by its inhibitor IκBε, confirmed by mouse knockouts. Using live-cell fluorescence microscopy, we showed that increased cRel primes B cells for early proliferation via higher basal expression of the cell cycle driver cMyc. However, peak cMyc induction amplitude is constrained by incoherent feedforward regulation, decoding the fold change of cRel activity to terminate the proliferative burst. This results in a complex nonlinear, nonmonotonic relationship between cRel expression and the extent of proliferation. These findings emphasize the importance of direct observational studies to complement gene knockout results and to learn about quantitative relationships between biological processes and their key regulators in the context of natural variations.
Collapse
Affiliation(s)
- Haripriya Vaidehi Narayanan
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA90095
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA90095
| | - Mark Y. Xiang
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA90095
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA90095
| | - Yijia Chen
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA90095
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA90095
| | - Helen Huang
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA90095
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA90095
| | - Sukanya Roy
- Division of Microbiology and Immunology, Department of Pathology, University of Utah, Salt Lake City, UT84112
| | - Himani Makkar
- Division of Microbiology and Immunology, Department of Pathology, University of Utah, Salt Lake City, UT84112
| | - Alexander Hoffmann
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA90095
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA90095
| | - Koushik Roy
- Division of Microbiology and Immunology, Department of Pathology, University of Utah, Salt Lake City, UT84112
| |
Collapse
|
4
|
Wang J, Fonseca GJ, Ding J. scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning. Nat Commun 2024; 15:5989. [PMID: 39013867 PMCID: PMC11252419 DOI: 10.1038/s41467-024-50150-1] [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] [Received: 11/29/2023] [Accepted: 06/28/2024] [Indexed: 07/18/2024] Open
Abstract
Single-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses. To overcome this challenge, we introduce "scSemiProfiler", an innovative computational framework that marries deep generative models with active learning strategies. This method adeptly infers single-cell profiles across large cohorts by fusing bulk sequencing data with targeted single-cell sequencing from a few rigorously chosen representatives. Extensive validation across heterogeneous datasets verifies the precision of our semi-profiling approach, aligning closely with true single-cell profiling data and empowering refined cellular analyses. Originally developed for extensive disease cohorts, "scSemiProfiler" is adaptable for broad applications. It provides a scalable, cost-effective solution for single-cell profiling, facilitating in-depth cellular investigation in various biological domains.
Collapse
Affiliation(s)
- Jingtao Wang
- Meakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Blvd, Montreal, H4A 3J1, Quebec, Canada
- Department of Medicine, Division of Experimental Medicine, McGill University, 1001 Decarie Blvd, Montreal, H4A 3J1, Quebec, Canada
| | - Gregory J Fonseca
- Meakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Blvd, Montreal, H4A 3J1, Quebec, Canada
- Department of Medicine, Division of Experimental Medicine, McGill University, 1001 Decarie Blvd, Montreal, H4A 3J1, Quebec, Canada
- Quantitative Life Sciences, McGill University, 845 Rue Sherbrooke Ouest, Montreal, H3A 0G4, Quebec, Canada
| | - Jun Ding
- Meakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Blvd, Montreal, H4A 3J1, Quebec, Canada.
- Department of Medicine, Division of Experimental Medicine, McGill University, 1001 Decarie Blvd, Montreal, H4A 3J1, Quebec, Canada.
- Quantitative Life Sciences, McGill University, 845 Rue Sherbrooke Ouest, Montreal, H3A 0G4, Quebec, Canada.
- School of Computer Science, McGill University, 3480 Rue University, Montreal, H3A 2A7, Quebec, Canada.
- Mila-Quebec AI Institute, 6666 Rue Saint-Urbain, Montreal, H2S 3H1, Quebec, Canada.
| |
Collapse
|
5
|
Chee FT, Harun S, Mohd Daud K, Sulaiman S, Nor Muhammad NA. Exploring gene regulation and biological processes in insects: Insights from omics data using gene regulatory network models. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2024; 189:1-12. [PMID: 38604435 DOI: 10.1016/j.pbiomolbio.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/18/2023] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
Gene regulatory network (GRN) comprises complicated yet intertwined gene-regulator relationships. Understanding the GRN dynamics will unravel the complexity behind the observed gene expressions. Insect gene regulation is often complicated due to their complex life cycles and diverse ecological adaptations. The main interest of this review is to have an update on the current mathematical modelling methods of GRNs to explain insect science. Several popular GRN architecture models are discussed, together with examples of applications in insect science. In the last part of this review, each model is compared from different aspects, including network scalability, computation complexity, robustness to noise and biological relevancy.
Collapse
Affiliation(s)
- Fong Ting Chee
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Sarahani Harun
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
| | - Suhaila Sulaiman
- FGV R&D Sdn Bhd, FGV Innovation Center, PT23417 Lengkuk Teknologi, Bandar Baru Enstek, 71760 Nilai, Negeri Sembilan, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
| |
Collapse
|
6
|
Van Eyndhoven LC, Vreezen CC, Tiemeijer BM, Tel J. Immune quorum sensing dictates IFN-I response dynamics in human plasmacytoid dendritic cells. Eur J Immunol 2024; 54:e2350955. [PMID: 38587967 DOI: 10.1002/eji.202350955] [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] [Received: 12/13/2023] [Revised: 03/26/2024] [Accepted: 03/29/2024] [Indexed: 04/10/2024]
Abstract
Type I interferons (IFN-Is) are key in fighting viral infections, but also serve major roles beyond antiviral immunity. Crucial is the tight regulation of IFN-I responses, while excessive levels are harmful to the cells. In essence, immune responses are generated by single cells making their own decisions, which are based on the signals they perceive. Additionally, immune cells must anticipate the future state of their environment, thereby weighing the costs and benefits of each possible outcome, in the presence of other potentially competitive decision makers (i.e., IFN-I producing cells). A rather new cellular communication mechanism called quorum sensing describes the effect of cell density on cellular secretory behaviors, which fits well with matching the right amount of IFN-Is produced to fight an infection. More competitive decision makers must contribute relatively less and vice versa. Intrigued by this concept, we assessed the effects of immune quorum sensing in pDCs, specialized immune cells known for their ability to mass produce IFN-Is. Using conventional microwell assays and droplet-based microfluidics assays, we were able the characterize the effect of quorum sensing in human primary immune cells in vitro. These insights open new avenues to manipulate IFN-I response dynamics in pathological conditions affected by aberrant IFN-I signaling.
Collapse
Affiliation(s)
- Laura C Van Eyndhoven
- Laboratory of Immunoengineering, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Cherise C Vreezen
- Laboratory of Immunoengineering, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Bart M Tiemeijer
- Laboratory of Immunoengineering, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Jurjen Tel
- Laboratory of Immunoengineering, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands
| |
Collapse
|
7
|
Moriel N, Memet E, Nitzan M. Optimal sequencing budget allocation for trajectory reconstruction of single cells. Bioinformatics 2024; 40:i446-i452. [PMID: 38940162 PMCID: PMC11211845 DOI: 10.1093/bioinformatics/btae258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations. RESULTS Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.
Collapse
Affiliation(s)
- Noa Moriel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Edvin Memet
- Department of Physics, Harvard University, Cambridge, MA 02138, United States
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| |
Collapse
|
8
|
Wang Y, Li Y, Zhu X, Yang M, Liu Y, Wang N, Long C, Kuo Y, Lian Y, Huang J, Jia J, Wong CCL, Yan Z, Yan L, Qiao J. Concurrent Preimplantation Genetic Testing and Competence Assessment of Human Embryos by Transcriptome Sequencing. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2309817. [PMID: 38900059 DOI: 10.1002/advs.202309817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/14/2024] [Indexed: 06/21/2024]
Abstract
Preimplantation genetic testing (PGT) can minimize the risk of birth defects. However, the accuracy and applicability of routine PGT is confounded by uneven genome coverage and high allele drop-out rate from existing single-cell whole genome amplification methods. Here, a method to diagnose genetic mutations and concurrently evaluate embryo competence by leveraging the abundant mRNA transcript copies present in trophectoderm cells is developed. The feasibility of the method is confirmed with 19 donated blastocysts. Next, the method is applied to 82 embryos from 26 families with monogenic defects for simultaneous mutation detection and competence assessment. The accuracy rate of direct mutation detection is up to 95%, which is significantly higher than DNA-based method. Meanwhile, this approach correctly predicted seven out of eight (87.5%) embryos that failed to implant. Of six embryos that are predicted to implant successfully, four met such expectations (66.7%). Notably, this method is superior at conditions for mutation detection that are challenging when using DNA-based PGT, such as when detecting pathogenic genes with a high de novo rate, multiple pseudogenes, or an abnormal expansion of CAG trinucleotide repeats. Taken together, this study establishes the feasibility of an RNA-based PGT that is also informative for assessing implantation competence.
Collapse
Affiliation(s)
- Yuqian Wang
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Ye Li
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Xiaohui Zhu
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
| | - Ming Yang
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Yujun Liu
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
| | - Nan Wang
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
| | - Chuan Long
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
| | - Ying Kuo
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
| | - Ying Lian
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
| | - Jin Huang
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
| | - Jialin Jia
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
| | - Catherine C L Wong
- Peking-Tsinghua Center for Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Zhiqiang Yan
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
| | - Liying Yan
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
| | - Jie Qiao
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, 100191, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China
- National Clinical Key Specialty Construction Program, Beijing, 100191, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| |
Collapse
|
9
|
SoRelle ED, Haynes LE, Willard KA, Chang B, Ch’ng J, Christofk H, Luftig MA. Epstein-Barr virus reactivation induces divergent abortive, reprogrammed, and host shutoff states by lytic progression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.14.598975. [PMID: 38915538 PMCID: PMC11195279 DOI: 10.1101/2024.06.14.598975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Viral infection leads to heterogeneous cellular outcomes ranging from refractory to abortive and fully productive states. Single cell transcriptomics enables a high resolution view of these distinct post-infection states. Here, we have interrogated the host-pathogen dynamics following reactivation of Epstein-Barr virus (EBV). While benign in most people, EBV is responsible for infectious mononucleosis, up to 2% of human cancers, and is a trigger for the development of multiple sclerosis. Following latency establishment in B cells, EBV reactivates and is shed in saliva to enable infection of new hosts. Beyond its importance for transmission, the lytic cycle is also implicated in EBV-associated oncogenesis. Conversely, induction of lytic reactivation in latent EBV-positive tumors presents a novel therapeutic opportunity. Therefore, defining the dynamics and heterogeneity of EBV lytic reactivation is a high priority to better understand pathogenesis and therapeutic potential. In this study, we applied single-cell techniques to analyze diverse fate trajectories during lytic reactivation in two B cell models. Consistent with prior work, we find that cell cycle and MYC expression correlate with cells refractory to lytic reactivation. We further found that lytic induction yields a continuum from abortive to complete reactivation. Abortive lytic cells upregulate NFκB and IRF3 pathway target genes, while cells that proceed through the full lytic cycle exhibit unexpected expression of genes associated with cellular reprogramming. Distinct subpopulations of lytic cells further displayed variable profiles for transcripts known to escape virus-mediated host shutoff. These data reveal previously unknown and promiscuous outcomes of lytic reactivation with broad implications for viral replication and EBV-associated oncogenesis.
Collapse
Affiliation(s)
- Elliott D. SoRelle
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, USA
- Duke Center for Virology, Durham, NC 27710, USA
| | - Lauren E. Haynes
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, USA
- Duke Center for Virology, Durham, NC 27710, USA
| | - Katherine A. Willard
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, USA
- Duke Center for Virology, Durham, NC 27710, USA
| | - Beth Chang
- Department of Integrative Immunobiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - James Ch’ng
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Heather Christofk
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90095, USA
| | - Micah A. Luftig
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, USA
- Duke Center for Virology, Durham, NC 27710, USA
| |
Collapse
|
10
|
Lin P, Gan YB, He J, Lin SE, Xu JK, Chang L, Zhao LM, Zhu J, Zhang L, Huang S, Hu O, Wang YB, Jin HJ, Li YY, Yan PL, Chen L, Jiang JX, Liu P. Advancing skeletal health and disease research with single-cell RNA sequencing. Mil Med Res 2024; 11:33. [PMID: 38816888 PMCID: PMC11138034 DOI: 10.1186/s40779-024-00538-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Orthopedic conditions have emerged as global health concerns, impacting approximately 1.7 billion individuals worldwide. However, the limited understanding of the underlying pathological processes at the cellular and molecular level has hindered the development of comprehensive treatment options for these disorders. The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized biomedical research by enabling detailed examination of cellular and molecular diversity. Nevertheless, investigating mechanisms at the single-cell level in highly mineralized skeletal tissue poses technical challenges. In this comprehensive review, we present a streamlined approach to obtaining high-quality single cells from skeletal tissue and provide an overview of existing scRNA-seq technologies employed in skeletal studies along with practical bioinformatic analysis pipelines. By utilizing these methodologies, crucial insights into the developmental dynamics, maintenance of homeostasis, and pathological processes involved in spine, joint, bone, muscle, and tendon disorders have been uncovered. Specifically focusing on the joint diseases of degenerative disc disease, osteoarthritis, and rheumatoid arthritis using scRNA-seq has provided novel insights and a more nuanced comprehension. These findings have paved the way for discovering novel therapeutic targets that offer potential benefits to patients suffering from diverse skeletal disorders.
Collapse
Grants
- 2022YFA1103202 National Key Research and Development Program of China
- 82272507 National Natural Science Foundation of China
- 32270887 National Natural Science Foundation of China
- 32200654 National Natural Science Foundation of China
- CSTB2023NSCQ-ZDJO008 Natural Science Foundation of Chongqing
- BX20220397 Postdoctoral Innovative Talent Support Program
- SFLKF202201 Independent Research Project of State Key Laboratory of Trauma and Chemical Poisoning
- 2021-XZYG-B10 General Hospital of Western Theater Command Research Project
- 14113723 University Grants Committee, Research Grants Council of Hong Kong, China
- N_CUHK472/22 University Grants Committee, Research Grants Council of Hong Kong, China
- C7030-18G University Grants Committee, Research Grants Council of Hong Kong, China
- T13-402/17-N University Grants Committee, Research Grants Council of Hong Kong, China
- AoE/M-402/20 University Grants Committee, Research Grants Council of Hong Kong, China
Collapse
Affiliation(s)
- Peng Lin
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Yi-Bo Gan
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Jian He
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
- Pancreatic Injury and Repair Key Laboratory of Sichuan Province, the General Hospital of Western Theater Command, Chengdu, 610031, China
| | - Si-En Lin
- Musculoskeletal Research Laboratory, Department of Orthopaedics & Traumatology, Faculty of Medicine, the Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, 999077, China
| | - Jian-Kun Xu
- Musculoskeletal Research Laboratory, Department of Orthopaedics & Traumatology, Faculty of Medicine, the Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, 999077, China
| | - Liang Chang
- Musculoskeletal Research Laboratory, Department of Orthopaedics & Traumatology, Faculty of Medicine, the Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, 999077, China
| | - Li-Ming Zhao
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Sacramento, CA, 94305, USA
| | - Jun Zhu
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Liang Zhang
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Sha Huang
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Ou Hu
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Ying-Bo Wang
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Huai-Jian Jin
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Yang-Yang Li
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Pu-Lin Yan
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Lin Chen
- Center of Bone Metabolism and Repair, State Key Laboratory of Trauma and Chemical Poisoning, Trauma Center, Research Institute of Surgery, Laboratory for the Prevention and Rehabilitation of Military Training Related Injuries, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Jian-Xin Jiang
- Wound Trauma Medical Center, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China.
| | - Peng Liu
- Department of Spine Surgery, Center of Orthopedics, State Key Laboratory of Trauma and Chemical Poisoning, Daping Hospital, Army Medical University, Chongqing, 400042, China.
| |
Collapse
|
11
|
Qin S, Zhang Y, Shi M, Miao D, Lu J, Wen L, Bai Y. In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level. Nat Commun 2024; 15:4387. [PMID: 38782922 PMCID: PMC11116506 DOI: 10.1038/s41467-024-48865-2] [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] [Received: 06/22/2023] [Accepted: 05/16/2024] [Indexed: 05/25/2024] Open
Abstract
Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.
Collapse
Affiliation(s)
- Shaojie Qin
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Yi Zhang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Mingying Shi
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Daiyu Miao
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Jiansen Lu
- Biomedical Pioneering Innovative Center, School of Life Sciences, Peking University, Beijing, China
| | - Lu Wen
- Biomedical Pioneering Innovative Center, School of Life Sciences, Peking University, Beijing, China
| | - Yu Bai
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
| |
Collapse
|
12
|
Liu W, Teng Z, Li Z, Chen J. CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data. Interdiscip Sci 2024:10.1007/s12539-024-00633-y. [PMID: 38778003 DOI: 10.1007/s12539-024-00633-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 05/25/2024]
Abstract
Gene regulatory network (GRN) inference based on single-cell RNA sequencing data (scRNAseq) plays a crucial role in understanding the regulatory mechanisms between genes. Various computational methods have been employed for GRN inference, but their performance in terms of network accuracy and model generalization is not satisfactory, and their poor performance is caused by high-dimensional data and network sparsity. In this paper, we propose a self-supervised method for gene regulatory network inference using single-cell RNA sequencing data (CVGAE). CVGAE uses graph neural network for inductive representation learning, which merges gene expression data and observed topology into a low-dimensional vector space. The well-trained vectors will be used to calculate mathematical distance of each gene, and further predict interactions between genes. In overall framework, FastICA is implemented to relief computational complexity caused by high dimensional data, and CVGAE adopts multi-stacked GraphSAGE layers as an encoder and an improved decoder to overcome network sparsity. CVGAE is evaluated on several single cell datasets containing four related ground-truth networks, and the result shows that CVGAE achieve better performance than comparative methods. To validate learning and generalization capabilities, CVGAE is applied in few-shot environment by change the ratio of train set and test set. In condition of few-shot, CVGAE obtains comparable or superior performance.
Collapse
Affiliation(s)
- Wei Liu
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China.
| | - Zhijie Teng
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Zejun Li
- School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang, 412002, China
| | - Jing Chen
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
| |
Collapse
|
13
|
Nguyen H, Nguyen H, Tran D, Draghici S, Nguyen T. Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges. Nucleic Acids Res 2024; 52:4761-4783. [PMID: 38619038 PMCID: PMC11109966 DOI: 10.1093/nar/gkae267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/01/2024] [Accepted: 04/02/2024] [Indexed: 04/16/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).
Collapse
Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| | - Ha Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| | - Duc Tran
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, USA
- Advaita Bioinformatics, Ann Arbor, MI, USA
| | - Tin Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| |
Collapse
|
14
|
Guan R, Li C, Gu F, Li W, Wei D, Cao S, Chang F, Lei D. Single-cell transcriptomic landscape and the microenvironment of normal adjacent tissues in hypopharyngeal carcinoma. BMC Genomics 2024; 25:489. [PMID: 38760729 PMCID: PMC11100249 DOI: 10.1186/s12864-024-10321-2] [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] [Received: 11/10/2023] [Accepted: 04/18/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND The cellular origin of hypopharyngeal diseases is crucial for further diagnosis and treatment, and the microenvironment in tissues may also be associated with specific cell types at the same time. Normal adjacent tissues (NATs) of hypopharyngeal carcinoma differ from non-tumor-bearing tissues, and can influenced by the tumor. However, the heterogeneity in kinds of disease samples remains little known, and the transcriptomic profile about biological information associated with disease occurrence and clinical outcome contained in it has yet to be fully evaluated. For these reasons, we should quickly investigate the taxonomic and transcriptomic information of NATs in human hypopharynx. RESULTS Single-cell suspensions of normal adjacent tissues (NATs) of hypopharyngeal carcinoma were obtained and single-cell RNA sequencing (scRNA-seq) was performed. We present scRNA-seq data from 39,315 high-quality cells in the hypopharyngeal from five human donors, nine clusters of normal adjacent human hypopharyngeal cells were presented, including epithelial cells, endothelial cells (ECs), mononuclear phagocyte system cells (MPs), fibroblasts, T cells, plasma cells, B cells, mural cells and mast cells. Nonimmune components in the microenvironment, including epithelial cells, endothelial cells, fibroblasts and the subpopulations of them were performed. CONCLUSIONS Our data provide a solid basis for the study of single-cell landscape in human normal adjacent hypopharyngeal tissues biology and related diseases.
Collapse
Affiliation(s)
- Rui Guan
- Department of Otorhinolaryngology, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Qilu Hospital, Cheeloo College of Medicine, Shandong University, 107 West Wenhua Road, Shandong, 250012, China
- Cheeloo College of Medicine, Shandong University, Jinan , Shandong, 250012, China
| | - Ce Li
- Department of Otorhinolaryngology, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Qilu Hospital, Cheeloo College of Medicine, Shandong University, 107 West Wenhua Road, Shandong, 250012, China
| | - Fangmeng Gu
- Department of Otorhinolaryngology, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Qilu Hospital, Cheeloo College of Medicine, Shandong University, 107 West Wenhua Road, Shandong, 250012, China
| | - Wenming Li
- Department of Otorhinolaryngology, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Qilu Hospital, Cheeloo College of Medicine, Shandong University, 107 West Wenhua Road, Shandong, 250012, China
| | - Dongmin Wei
- Department of Otorhinolaryngology, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Qilu Hospital, Cheeloo College of Medicine, Shandong University, 107 West Wenhua Road, Shandong, 250012, China
| | - Shengda Cao
- Department of Otorhinolaryngology, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Qilu Hospital, Cheeloo College of Medicine, Shandong University, 107 West Wenhua Road, Shandong, 250012, China
| | - Fen Chang
- Department of Otorhinolaryngology, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Qilu Hospital, Cheeloo College of Medicine, Shandong University, 107 West Wenhua Road, Shandong, 250012, China
| | - Dapeng Lei
- Department of Otorhinolaryngology, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Qilu Hospital, Cheeloo College of Medicine, Shandong University, 107 West Wenhua Road, Shandong, 250012, China.
- Cheeloo College of Medicine, Shandong University, Jinan , Shandong, 250012, China.
| |
Collapse
|
15
|
Moore KM, Pelletier AN, Lapp S, Metz A, Tharp GK, Lee M, Bhasin SS, Bhasin M, Sékaly RP, Bosinger SE, Suthar MS. Single-cell analysis reveals an antiviral network that controls Zika virus infection in human dendritic cells. J Virol 2024; 98:e0019424. [PMID: 38567950 PMCID: PMC11092337 DOI: 10.1128/jvi.00194-24] [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] [Received: 01/30/2024] [Accepted: 03/12/2024] [Indexed: 04/16/2024] Open
Abstract
Zika virus (ZIKV) is a mosquito-borne flavivirus that caused an epidemic in the Americas in 2016 and is linked to severe neonatal birth defects, including microcephaly and spontaneous abortion. To better understand the host response to ZIKV infection, we adapted the 10× Genomics Chromium single-cell RNA sequencing (scRNA-seq) assay to simultaneously capture viral RNA and host mRNA. Using this assay, we profiled the antiviral landscape in a population of human monocyte-derived dendritic cells infected with ZIKV at the single-cell level. The bystander cells, which lacked detectable viral RNA, expressed an antiviral state that was enriched for genes coinciding predominantly with a type I interferon (IFN) response. Within the infected cells, viral RNA negatively correlated with type I IFN-dependent and -independent genes (the antiviral module). We modeled the ZIKV-specific antiviral state at the protein level, leveraging experimentally derived protein interaction data. We identified a highly interconnected network between the antiviral module and other host proteins. In this work, we propose a new paradigm for evaluating the antiviral response to a specific virus, combining an unbiased list of genes that highly correlate with viral RNA on a per-cell basis with experimental protein interaction data. IMPORTANCE Zika virus (ZIKV) remains a public health threat given its potential for re-emergence and the detrimental fetal outcomes associated with infection during pregnancy. Understanding the dynamics between ZIKV and its host is critical to understanding ZIKV pathogenesis. Through ZIKV-inclusive single-cell RNA sequencing (scRNA-seq), we demonstrate on the single-cell level the dynamic interplay between ZIKV and the host: the transcriptional program that restricts viral infection and ZIKV-mediated inhibition of that response. Our ZIKV-inclusive scRNA-seq assay will serve as a useful tool for gaining greater insight into the host response to ZIKV and can be applied more broadly to the flavivirus field.
Collapse
Affiliation(s)
- Kathryn M. Moore
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
| | | | - Stacey Lapp
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
| | - Amanda Metz
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
| | - Gregory K. Tharp
- Emory National Primate Research Center, Atlanta, Georgia, USA
- Emory NPRC Genomics Core Laboratory, Atlanta, Georgia, USA
| | - Michelle Lee
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
| | - Swati Sharma Bhasin
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta and Department of Pediatrics, Emory University, Atlanta, Georgia, USA
| | - Manoj Bhasin
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta and Department of Pediatrics, Emory University, Atlanta, Georgia, USA
| | - Rafick-Pierre Sékaly
- Emory Vaccine Center, Atlanta, Georgia, USA
- Pathology Advanced Translational Research Unit, Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Steven E. Bosinger
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
- Emory NPRC Genomics Core Laboratory, Atlanta, Georgia, USA
| | - Mehul S. Suthar
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
- Department of Microbiology and Immunology, Emory University, Atlanta, Georgia, USA
| |
Collapse
|
16
|
Singh A, Khiabanian H. Feature selection followed by a novel residuals-based normalization simplifies and improves single-cell gene expression analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.02.530891. [PMID: 38328133 PMCID: PMC10849523 DOI: 10.1101/2023.03.02.530891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are to reduce the systematic biases primarily introduced through technical sources and to transform the data to make it more amenable for application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo.
Collapse
Affiliation(s)
- Amartya Singh
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, New Jersey
| | - Hossein Khiabanian
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, New Jersey
- Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey
| |
Collapse
|
17
|
Cuevas-Diaz Duran R, Wei H, Wu J. Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets. BMC Genomics 2024; 25:444. [PMID: 38711017 PMCID: PMC11073985 DOI: 10.1186/s12864-024-10364-5] [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] [Received: 09/02/2023] [Accepted: 04/29/2024] [Indexed: 05/08/2024] Open
Abstract
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 appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis. CONCLUSIONS According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.
Collapse
Affiliation(s)
- Raquel Cuevas-Diaz Duran
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo Leon, 64710, Mexico.
| | - Haichao Wei
- The Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Center for Stem Cell and Regenerative Medicine, UT Brown Foundation Institute of Molecular Medicine, Houston, TX, 77030, USA
| | - Jiaqian Wu
- The Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
- Center for Stem Cell and Regenerative Medicine, UT Brown Foundation Institute of Molecular Medicine, Houston, TX, 77030, USA.
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
| |
Collapse
|
18
|
Stock M, Popp N, Fiorentino J, Scialdone A. Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data. Bioinformatics 2024; 40:btae267. [PMID: 38627250 PMCID: PMC11096270 DOI: 10.1093/bioinformatics/btae267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 02/28/2024] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
MOTIVATION In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs. RESULTS To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest. AVAILABILITY AND IMPLEMENTATION STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.
Collapse
Affiliation(s)
- Marco Stock
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich 85354, Germany
| | - Niclas Popp
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| | - Jonathan Fiorentino
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| | - Antonio Scialdone
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| |
Collapse
|
19
|
Zhang X, Zhu R, Yu D, Wang J, Yan Y, Xu K. Single-cell RNA sequencing to explore cancer-associated fibroblasts heterogeneity: "Single" vision for "heterogeneous" environment. Cell Prolif 2024; 57:e13592. [PMID: 38158643 PMCID: PMC11056715 DOI: 10.1111/cpr.13592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 10/24/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024] Open
Abstract
Cancer-associated fibroblasts (CAFs), a phenotypically and functionally heterogeneous stromal cell, are one of the most important components of the tumour microenvironment. Previous studies have consolidated it as a promising target against cancer. However, variable therapeutic efficacy-both protumor and antitumor effects have been observed not least owing to the strong heterogeneity of CAFs. Over the past 10 years, advances in single-cell RNA sequencing (scRNA-seq) technologies had a dramatic effect on biomedical research, enabling the analysis of single cell transcriptomes with unprecedented resolution and throughput. Specifically, scRNA-seq facilitates our understanding of the complexity and heterogeneity of diverse CAF subtypes. In this review, we discuss the up-to-date knowledge about CAF heterogeneity with a focus on scRNA-seq perspective to investigate the emerging strategies for integrating multimodal single-cell platforms. Furthermore, we summarized the clinical application of scRNA-seq on CAF research. We believe that the comprehensive understanding of the heterogeneity of CAFs form different visions will generate innovative solutions to cancer therapy and achieve clinical applications.
Collapse
Affiliation(s)
- Xiangjian Zhang
- The Dingli Clinical College of Wenzhou Medical UniversityWenzhouZhejiangChina
- Department of Surgical OncologyWenzhou Central HospitalWenzhouZhejiangChina
- The Second Affiliated Hospital of Shanghai UniversityWenzhouZhejiangChina
| | - Ruiqiu Zhu
- Interventional Cancer Institute of Chinese Integrative MedicinePutuo Hospital, Shanghai University of Traditional Chinese MedicineShanghaiChina
| | - Die Yu
- Interventional Cancer Institute of Chinese Integrative MedicinePutuo Hospital, Shanghai University of Traditional Chinese MedicineShanghaiChina
| | - Juan Wang
- School of MedicineShanghai UniversityShanghaiChina
| | - Yuxiang Yan
- The Dingli Clinical College of Wenzhou Medical UniversityWenzhouZhejiangChina
- Department of Surgical OncologyWenzhou Central HospitalWenzhouZhejiangChina
- The Second Affiliated Hospital of Shanghai UniversityWenzhouZhejiangChina
| | - Ke Xu
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
- Wenzhou Institute of Shanghai UniversityWenzhouChina
| |
Collapse
|
20
|
Ramirez Flores RO, Schäfer PSL, Küchenhoff L, Saez-Rodriguez J. Complementing Cell Taxonomies with a Multicellular Analysis of Tissues. Physiology (Bethesda) 2024; 39:0. [PMID: 38319138 DOI: 10.1152/physiol.00001.2024] [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] [Received: 01/03/2024] [Accepted: 01/31/2024] [Indexed: 02/07/2024] Open
Abstract
The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.
Collapse
Affiliation(s)
- Ricardo Omar Ramirez Flores
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Sven Lars Schäfer
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Leonie Küchenhoff
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| |
Collapse
|
21
|
Sun H, Chang Z, Li H, Tang Y, Liu Y, Qiao L, Feng G, Huang R, Han D, Yin DT. Multi-omics analysis-based macrophage differentiation-associated papillary thyroid cancer patient classifier. Transl Oncol 2024; 43:101889. [PMID: 38382228 PMCID: PMC10900934 DOI: 10.1016/j.tranon.2024.101889] [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: 10/11/2023] [Revised: 01/02/2024] [Accepted: 01/21/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND The reclassification of Papillary Thyroid Carcinoma (PTC) is an area of research that warrants attention. The connection between thyroid cancer, inflammation, and immune responses necessitates considering the mechanisms of differential prognosis of thyroid tumors from an immunological perspective. Given the high adaptability of macrophages to environmental stimuli, focusing on the differentiation characteristics of macrophages might offer a novel approach to address the issues related to PTC subtyping. METHODS Single-cell RNA sequencing data of medullary cells infiltrated by papillary thyroid carcinoma obtained from public databases was subjected to dimensionality reduction clustering analysis. The RunUMAP and FindAllMarkers functions were utilized to identify the gene expression matrix of different clusters. Cell differentiation trajectory analysis was conducted using the Monocle R package. A complex regulatory network for the classification of Immune status and Macrophage differentiation-associated Papillary Thyroid Cancer Classification (IMPTCC) was constructed through quantitative multi-omics analysis. Immunohistochemistry (IHC) staining was utilized for pathological histology validation. RESULTS Through the integration of single-cell RNA and bulk sequencing data combined with multi-omics analysis, we identified crucial transcription factors, immune cells/immune functions, and signaling pathways. Based on this, regulatory networks for three IMPTCC clusters were established. CONCLUSION Based on the co-expression network analysis results, we identified three subtypes of IMPTCC: Immune-Suppressive Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (ISMPTCC), Immune-Neutral Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (INMPTCC), and Immune-Activated Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (IAMPTCC). Each subtype exhibits distinct metabolic, immune, and regulatory characteristics corresponding to different states of macrophage differentiation.
Collapse
Affiliation(s)
- Hanlin Sun
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, PR China
| | - Zhengyan Chang
- Department of Pathology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, PR China
| | - Hongqiang Li
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, PR China
| | - Yifeng Tang
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, PR China
| | - Yihao Liu
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, PR China
| | - Lixue Qiao
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, PR China
| | - Guicheng Feng
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, PR China
| | - Runzhi Huang
- Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, PR China.
| | - Dongyan Han
- Department of Pathology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, PR China.
| | - De-Tao Yin
- Department of Thyroid Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, PR China; Engineering Research Center of Multidisciplinary Diagnosis and Treatment of Thyroid Cancer of Henan Province, Zhengzhou 450052, Henan, PR China; Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou 450052, Henan, PR China.
| |
Collapse
|
22
|
Bridges K, Pizzurro GA, Khunte M, Chen M, Salvador Rocha E, Alexander AF, Bass V, Kellman LN, Baskaran J, Miller-Jensen K. Single-Cell Analysis Reveals a Subset of High IL-12p40-Secreting Dendritic Cells within Mouse Bone Marrow-Derived Macrophages Differentiated with M-CSF. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2024; 212:1357-1365. [PMID: 38416039 DOI: 10.4049/jimmunol.2300431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 02/01/2024] [Indexed: 02/29/2024]
Abstract
Macrophages and dendritic cells (DCs), although ontogenetically distinct, have overlapping functions and exhibit substantial cell-to-cell heterogeneity that can complicate their identification and obscure innate immune function. In this study, we report that M-CSF-differentiated murine bone marrow-derived macrophages (BMDMs) exhibit extreme heterogeneity in the production of IL-12, a key proinflammatory cytokine linking innate and adaptive immunity. A microwell secretion assay revealed that a small fraction of BMDMs stimulated with LPS secrete most IL-12p40, and we confirmed that this is due to extremely high expression of Il12b, the gene encoding IL-12p40, in a subset of cells. Using an Il12b-YFP reporter mouse, we isolated cells with high LPS-induced Il12b expression and found that this subset was enriched for genes associated with the DC lineage. Single-cell RNA sequencing data confirmed a DC-like subset that differentiates within BMDM cultures that is transcriptionally distinct but could not be isolated by surface marker expression. Although not readily apparent in the resting state, upon LPS stimulation, this subset exhibited a typical DC-associated activation program that is distinct from LPS-induced stochastic BMDM cell-to-cell heterogeneity. Overall, our findings underscore the difficulty in distinguishing macrophages and DCs even in widely used in vitro murine BMDM cultures and could affect the interpretation of some studies that use BMDMs to explore acute inflammatory responses.
Collapse
Affiliation(s)
- Kate Bridges
- Department of Biomedical Engineering, Yale University, New Haven, CT
| | | | - Mihir Khunte
- Department of Biomedical Engineering, Yale University, New Haven, CT
| | - Meibin Chen
- Department of Biomedical Engineering, Yale University, New Haven, CT
| | | | | | - Victor Bass
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT
| | - Laura N Kellman
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT
| | - Janani Baskaran
- Department of Biomedical Engineering, Yale University, New Haven, CT
| | - Kathryn Miller-Jensen
- Department of Biomedical Engineering, Yale University, New Haven, CT
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT
| |
Collapse
|
23
|
Caron DP, Specht WL, Chen D, Wells SB, Szabo PA, Jensen IJ, Farber DL, Sims PA. Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.06.547944. [PMID: 37461466 PMCID: PMC10350048 DOI: 10.1101/2023.07.06.547944] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and dissecting transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins, as in cells of the immune system. Cellular Indexing of Transcriptomes and Epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell type annotation requires a classifier that integrates multimodal data. Here, we describe MultiModal Classifier Hierarchy (MMoCHi), a marker-based approach for classification, reconciling gene and protein expression without reliance on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal novel subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities.
Collapse
Affiliation(s)
- Daniel P. Caron
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - William L. Specht
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - David Chen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Steven B. Wells
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Peter A. Szabo
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Isaac J. Jensen
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Donna L. Farber
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Peter A. Sims
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
| |
Collapse
|
24
|
Ren L, Huang D, Liu H, Ning L, Cai P, Yu X, Zhang Y, Luo N, Lin H, Su J, Zhang Y. Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review). Oncol Lett 2024; 27:152. [PMID: 38406595 PMCID: PMC10885005 DOI: 10.3892/ol.2024.14285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.
Collapse
Affiliation(s)
- Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Danni Huang
- Department of Radiology, Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208, P.R. China
| | - Hongjiang Liu
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, Sichuan 610106, P.R. China
| | - Xiaolong Yu
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Research Institute, Material Science and Engineering Institute of Hainan University, Sanya, Hainan 572025, P.R. China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China
| | - Jinsong Su
- Research Institute of Integrated Traditional Chinese Medicine and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Yinghui Zhang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| |
Collapse
|
25
|
Van Eyndhoven LC, Chouri E, Matos CI, Pandit A, Radstake TRDJ, Broen JCA, Singh A, Tel J. Unraveling IFN-I response dynamics and TNF crosstalk in the pathophysiology of systemic lupus erythematosus. Front Immunol 2024; 15:1322814. [PMID: 38596672 PMCID: PMC11002168 DOI: 10.3389/fimmu.2024.1322814] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
Introduction The innate immune system serves the crucial first line of defense against a wide variety of potential threats, during which the production of pro-inflammatory cytokines IFN-I and TNFα are key. This astonishing power to fight invaders, however, comes at the cost of risking IFN-I-related pathologies, such as observed during autoimmune diseases, during which IFN-I and TNFα response dynamics are dysregulated. Therefore, these response dynamics must be tightly regulated, and precisely matched with the potential threat. This regulation is currently far from understood. Methods Using droplet-based microfluidics and ODE modeling, we studied the fundamentals of single-cell decision-making upon TLR signaling in human primary immune cells (n = 23). Next, using biologicals used for treating autoimmune diseases [i.e., anti-TNFα, and JAK inhibitors], we unraveled the crosstalk between IFN-I and TNFα signaling dynamics. Finally, we studied primary immune cells isolated from SLE patients (n = 8) to provide insights into SLE pathophysiology. Results single-cell IFN-I and TNFα response dynamics display remarkable differences, yet both being highly heterogeneous. Blocking TNFα signaling increases the percentage of IFN-I-producing cells, while blocking IFN-I signaling decreases the percentage of TNFα-producing cells. Single-cell decision-making in SLE patients is dysregulated, pointing towards a dysregulated crosstalk between IFN-I and TNFα response dynamics. Discussion We provide a solid droplet-based microfluidic platform to study inherent immune secretory behaviors, substantiated by ODE modeling, which can challenge the conceptualization within and between different immune signaling systems. These insights will build towards an improved fundamental understanding on single-cell decision-making in health and disease.
Collapse
Affiliation(s)
- Laura C. Van Eyndhoven
- Laboratory of Immunoengineering, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, Netherlands
| | - Eleni Chouri
- Laboratory of Immunoengineering, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, Netherlands
| | - Catarina I. Matos
- Laboratory of Immunoengineering, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, Netherlands
| | - Aridaman Pandit
- Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Timothy R. D. J. Radstake
- Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jasper C. A. Broen
- Regional Rheumatology Center, Máxima Medical Center, Eindhoven and Veldhoven, Eindhoven, Netherlands
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, United States
| | - Jurjen Tel
- Laboratory of Immunoengineering, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, Netherlands
| |
Collapse
|
26
|
Lin S, Feng D, Han X, Li L, Lin Y, Gao H. Microfluidic platform for omics analysis on single cells with diverse morphology and size: A review. Anal Chim Acta 2024; 1294:342217. [PMID: 38336406 DOI: 10.1016/j.aca.2024.342217] [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: 08/29/2023] [Revised: 01/04/2024] [Accepted: 01/04/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Microfluidic techniques have emerged as powerful tools in single-cell research, facilitating the exploration of omics information from individual cells. Cell morphology is crucial for gene expression and physiological processes. However, there is currently a lack of integrated analysis of morphology and single-cell omics information. A critical challenge remains: what platform technologies are the best option to decode omics data of cells that are complex in morphology and size? RESULTS This review highlights achievements in microfluidic-based single-cell omics and isolation of cells based on morphology, along with other cell sorting methods based on physical characteristics. Various microfluidic platforms for single-cell isolation are systematically presented, showcasing their diversity and adaptability. The discussion focuses on microfluidic devices tailored to the distinct single-cell isolation requirements in plants and animals, emphasizing the significance of considering cell morphology and cell size in optimizing single-cell omics strategies. Simultaneously, it explores the application of microfluidic single-cell sorting technologies to single-cell sequencing, aiming to effectively integrate information about cell shape and size. SIGNIFICANCE AND NOVELTY The novelty lies in presenting a comprehensive overview of recent accomplishments in microfluidic-based single-cell omics, emphasizing the integration of different microfluidic platforms and their implications for cell morphology-based isolation. By underscoring the pivotal role of the specialized morphology of different cells in single-cell research, this review provides robust support for delving deeper into the exploration of single-cell omics data.
Collapse
Affiliation(s)
- Shujin Lin
- Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China; Central Laboratory at the Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine, Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, China
| | - Dan Feng
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiao Han
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China.
| | - Ling Li
- Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China; The First Clinical Medical College of Fujian Medical University, Fuzhou, 350004, China; Hepatopancreatobiliary Surgery Department, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350004, China.
| | - Yao Lin
- Central Laboratory at the Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine, Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, China; Collaborative Innovation Center for Rehabilitation Technology, Fujian University of Traditional Chinese Medicine, China.
| | - Haibing Gao
- Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China.
| |
Collapse
|
27
|
Zou M, Zheng Z, Xiahou Z, Cao J. Prediction of potential targets and toxicological insights of Astragalus in liver cancer based on network pharmacology: Integrating systems biology, drug interaction networks, and toxicological perspectives. ENVIRONMENTAL TOXICOLOGY 2024. [PMID: 38476113 DOI: 10.1002/tox.24189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/31/2024] [Accepted: 02/10/2024] [Indexed: 03/14/2024]
Abstract
This study investigates Astragalus's efficacy as a novel therapeutic option for primary liver cancer (PLC), capitalizing on its anti-inflammatory and antiviral effects. We utilized network pharmacology to unveil Astragalus's potential targets against PLC, revealing significant gene expression alterations in treated samples-20 genes were up-regulated, and 20 were down-regulated compared to controls. Our analysis extended to single-cell resolution, where we processed scRNA-seq data to discern 15 unique cell clusters within the immune, malignant, and stromal compartments through advanced algorithms like UMAP and tSNE. To delve deeper into the functional implications of these gene expression changes, we conducted comprehensive gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, alongside Gene Set Variation Analysis, to elucidate the biological processes and pathways involved. Further, we constructed protein-protein interaction networks to visualize the intricate molecular interplay, highlighting the down-regulation of MT1E in PLC cells, a finding corroborated by quantitative polymerase chain reaction. Molecular docking studies affirmed the potent interaction between Astragalus's active compounds and MT proteins, underscoring a targeted therapeutic mechanism. Our investigation also encompassed a detailed cellular landscape analysis, identifying nine cell subgroups related to MT1 expression and specifying five cell subsets through the SingleR package. Advanced trajectory and cell-cell interaction analyses offered deeper insights into the dynamics of MT1-associated cellular subpopulations. This comprehensive methodology not only underpins Astragalus's promising role in PLC treatment but also advances our understanding of its molecular and cellular mechanisms, paving the way for targeted therapeutic strategies.
Collapse
Affiliation(s)
- Minjun Zou
- Zhongshan People's Hospital, Zhongshan, Guangdong, China
| | - Zhiye Zheng
- Zhongshan People's Hospital, Zhongshan, Guangdong, China
| | - Zhikai Xiahou
- China Institute of Sport and Health Science, Beijing Sport University, Beijing, China
| | - Jianwei Cao
- Zhongshan People's Hospital, Zhongshan, Guangdong, China
| |
Collapse
|
28
|
Garma LD, Osório NS. Demystifying dimensionality reduction techniques in the 'omics' era: A practical approach for biological science students. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2024; 52:165-178. [PMID: 37937712 DOI: 10.1002/bmb.21800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023]
Abstract
Dimensionality reduction techniques are essential in analyzing large 'omics' datasets in biochemistry and molecular biology. Principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are commonly used for data visualization. However, these methods can be challenging for students without a strong mathematical background. In this study, intuitive examples were created using COVID-19 data to help students understand the core concepts behind these techniques. In a 4-h practical session, we used these examples to demonstrate dimensionality reduction techniques to 15 postgraduate students from biomedical backgrounds. Using Python and Jupyter notebooks, our goal was to demystify these methods, typically treated as "black boxes", and empower students to generate and interpret their own results. To assess the impact of our approach, we conducted an anonymous survey. The majority of the students agreed that using computers enriched their learning experience (67%) and that Jupyter notebooks were a valuable part of the class (66%). Additionally, 60% of the students reported increased interest in Python, and 40% gained both interest and a better understanding of dimensionality reduction methods. Despite the short duration of the course, 40% of the students reported acquiring research skills necessary in the field. While further analysis of the learning impacts of this approach is needed, we believe that sharing the examples we generated can provide valuable resources for others to use in interactive teaching environments. These examples highlight advantages and limitations of the major dimensionality reduction methods used in modern bioinformatics analysis in an easy-to-understand way.
Collapse
Affiliation(s)
- Leonardo D Garma
- Breast Cancer Clinical Research Unit, Centro Nacional de Investigaciones Oncológicas - CNIO, Madrid, Spain
| | - Nuno S Osório
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's -PT Government Associate Laboratory, Braga, Portugal
| |
Collapse
|
29
|
Chen J, Wu J, Bai Y, Yang C, Wang J. Recent advances of single-cell RNA sequencing in toxicology research: Insight into hepatotoxicity and nephrotoxicity. CURRENT OPINION IN TOXICOLOGY 2024; 37:100462. [DOI: 10.1016/j.cotox.2024.100462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2024]
|
30
|
Labib M, Wang Z, Kim Y, Lin S, Abdrabou A, Yousefi H, Lo PY, Angers S, Sargent EH, Kelley SO. Identification of druggable regulators of cell secretion via a kinome-wide screen and high-throughput immunomagnetic cell sorting. Nat Biomed Eng 2024; 8:263-277. [PMID: 38012306 DOI: 10.1038/s41551-023-01135-w] [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: 03/18/2022] [Accepted: 10/16/2023] [Indexed: 11/29/2023]
Abstract
The identification of genetic regulators of cell secretions is challenging because it requires the sorting of a large number of cells according to their secretion patterns. Here we report the development and applicability of a high-throughput microfluidic method for the analysis of the secretion levels of large populations of immune cells. The method is linked with a kinome-wide loss-of-function CRISPR screen, immunomagnetically sorting the cells according to their secretion levels, and the sequencing of their genomes to identify key genetic modifiers of cell secretion. We used the method, which we validated against flow cytometry for cytokines secreted from primary mouse CD4+ (cluster of differentiation 4-positive) T cells, to discover a subgroup of highly co-expressed kinase-coding genes that regulate interferon-gamma secretion by these cells. We validated the function of the kinases identified using RNA interference, CRISPR knockouts and kinase inhibitors and confirmed the druggability of selected kinases via the administration of a kinase inhibitor in an animal model of colitis. The technique may facilitate the discovery of regulatory mechanisms for immune-cell activation and of therapeutic targets for autoimmune diseases.
Collapse
Affiliation(s)
- Mahmoud Labib
- Peninsula Medical School, Faculty of Health, University of Plymouth, Plymouth, UK
- Department of Chemistry, Northwestern University, Evanston, IL, USA
- Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Zongjie Wang
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Yunhye Kim
- Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Sichun Lin
- Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Abdalla Abdrabou
- Robert H. Laurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA
| | - Hanie Yousefi
- Department of Chemistry, Northwestern University, Evanston, IL, USA
| | - Pei-Ying Lo
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Stéphane Angers
- Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada
| | - Edward H Sargent
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
- Department of Electrical & Computer Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Shana O Kelley
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
- Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada.
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
- Robert H. Laurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA.
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada.
- Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada.
- Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
- Chan Zuckerberg Biohub Chicago, Chicago, IL, USA.
| |
Collapse
|
31
|
Choi JM, Park C, Chae H. moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud. PeerJ 2024; 12:e17006. [PMID: 38426141 PMCID: PMC10903350 DOI: 10.7717/peerj.17006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets-gene expression, DNA methylation, and DNA accessibility-while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer's superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.
Collapse
Affiliation(s)
- Joung Min Choi
- Department of Computer Science, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, Virginia, United States
| | - Chaelin Park
- Division of Computer Science, Sookmyung Women’s University, Seoul, South Korea
| | - Heejoon Chae
- Division of Computer Science, Sookmyung Women’s University, Seoul, South Korea
| |
Collapse
|
32
|
Zhang W, Huckaby B, Talburt J, Weissman S, Yang MQ. cnnImpute: missing value recovery for single cell RNA sequencing data. Sci Rep 2024; 14:3946. [PMID: 38365936 PMCID: PMC10873334 DOI: 10.1038/s41598-024-53998-x] [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] [Received: 11/06/2023] [Accepted: 02/07/2024] [Indexed: 02/18/2024] Open
Abstract
The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized our ability to explore cellular diversity and unravel the complexities of intricate diseases. However, due to the inherently low signal-to-noise ratio and the presence of an excessive number of missing values, scRNA-seq data analysis encounters unique challenges. Here, we present cnnImpute, a novel convolutional neural network (CNN) based method designed to address the issue of missing data in scRNA-seq. Our approach starts by estimating missing probabilities, followed by constructing a CNN-based model to recover expression values with a high likelihood of being missing. Through comprehensive evaluations, cnnImpute demonstrates its effectiveness in accurately imputing missing values while preserving the integrity of cell clusters in scRNA-seq data analysis. It achieved superior performance in various benchmarking experiments. cnnImpute offers an accurate and scalable method for recovering missing values, providing a useful resource for scRNA-seq data analysis.
Collapse
Affiliation(s)
- Wenjuan Zhang
- MidSouth Bioinformatics Center and Joint Bioinformatics Graduate Program, University of Arkansas at Little Rock, University of Arkansas for Medical Sciences, Little Rock, 72204, AR, USA
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, 72204, AR, USA
| | - Brandon Huckaby
- Department of Computer Science, University of Arkansas at Little Rock, Little Rock, 72204, AR, USA
| | - John Talburt
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, 72204, AR, USA
| | - Sherman Weissman
- Department of Genetics, Yale School of Medicine, New Haven, 06520, CT, USA
| | - Mary Qu Yang
- MidSouth Bioinformatics Center and Joint Bioinformatics Graduate Program, University of Arkansas at Little Rock, University of Arkansas for Medical Sciences, Little Rock, 72204, AR, USA.
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, 72204, AR, USA.
| |
Collapse
|
33
|
Walls AW, Rosenthal AZ. Bacterial phenotypic heterogeneity through the lens of single-cell RNA sequencing. Transcription 2024; 15:48-62. [PMID: 38532542 DOI: 10.1080/21541264.2024.2334110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 03/19/2024] [Indexed: 03/28/2024] Open
Abstract
Bacterial transcription is not monolithic. Microbes exist in a wide variety of cell states that help them adapt to their environment, acquire and produce essential nutrients, and engage in both competition and cooperation with their neighbors. While we typically think of bacterial adaptation as a group behavior, where all cells respond in unison, there is often a mixture of phenotypic responses within a bacterial population, where distinct cell types arise. A primary phenomenon driving these distinct cell states is transcriptional heterogeneity. Given that bacterial mRNA transcripts are extremely short-lived compared to eukaryotes, their transcriptional state is closely associated with their physiology, and thus the transcriptome of a bacterial cell acts as a snapshot of the behavior of that bacterium. Therefore, the application of single-cell transcriptomics to microbial populations will provide novel insight into cellular differentiation and bacterial ecology. In this review, we provide an overview of transcriptional heterogeneity in microbial systems, discuss the findings already provided by single-cell approaches, and plot new avenues of inquiry in transcriptional regulation, cellular biology, and mechanisms of heterogeneity that are made possible when microbial communities are analyzed at single-cell resolution.
Collapse
Affiliation(s)
- Alex W Walls
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA
| | - Adam Z Rosenthal
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA
| |
Collapse
|
34
|
da Silva JEH, de Carvalho PC, Camata JJ, de Oliveira IL, Bernardino HS. A Data-Distribution and Successive Spline Points based discretization approach for evolving gene regulatory networks from scRNA-Seq time-series data using Cartesian Genetic Programming. Biosystems 2024; 236:105126. [PMID: 38278505 DOI: 10.1016/j.biosystems.2024.105126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/18/2023] [Accepted: 01/19/2024] [Indexed: 01/28/2024]
Abstract
The inference of gene regulatory networks (GRNs) is a widely addressed problem in Systems Biology. GRNs can be modeled as Boolean networks, which is the simplest approach for this task. However, Boolean models need binarized data. Several approaches have been developed for the discretization of gene expression data (GED). Also, the advance of data extraction technologies, such as single-cell RNA-Sequencing (scRNA-Seq), provides a new vision of gene expression and brings new challenges for dealing with its specificities, such as a large occurrence of zero data. This work proposes a new discretization approach for dealing with scRNA-Seq time-series data, named Distribution and Successive Spline Points Discretization (DSSPD), which considers the data distribution and a proper preprocessing step. Here, Cartesian Genetic Programming (CGP) is used to infer GRNs using the results of DSSPD. The proposal is compared with CGP with the standard data handling and five state-of-the-art algorithms on curated models and experimental data. The results show that the proposal improves the results of CGP in all tested cases and outperforms the state-of-the-art algorithms in most cases.
Collapse
Affiliation(s)
| | | | - José J Camata
- Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil.
| | | | | |
Collapse
|
35
|
Jiang D, Chowdhury AY, Nogalska A, Contreras J, Lee Y, Vergel-Rodriguez M, Valenzuela M, Lu R. Quantitative association between gene expression and blood cell production of individual hematopoietic stem cells in mice. SCIENCE ADVANCES 2024; 10:eadk2132. [PMID: 38277455 PMCID: PMC10816716 DOI: 10.1126/sciadv.adk2132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/27/2023] [Indexed: 01/28/2024]
Abstract
Individual hematopoietic stem cells (HSCs) produce different amounts of blood cells upon transplantation. Taking advantage of the intercellular variation, we developed an experimental and bioinformatic approach to evaluating the quantitative association between gene expression and blood cell production across individual HSCs. We found that most genes associated with blood production exhibit the association only at some levels of blood production. By mapping gene expression with blood production, we identified four distinct patterns of their quantitative association. Some genes consistently correlate with blood production over a range of levels or across all levels, and these genes are found to regulate lymphoid but not myeloid production. Other genes exhibit one or more clear peaks of association. Genes with overlapping peaks are found to be coexpressed in other tissues and share similar molecular functions and regulatory motifs. By dissecting intercellular variations, our findings revealed four quantitative association patterns that reflect distinct dose-response molecular mechanisms modulating the blood cell production of HSCs.
Collapse
Affiliation(s)
- Du Jiang
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Adnan Y. Chowdhury
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Anna Nogalska
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Jorge Contreras
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Yeachan Lee
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Mary Vergel-Rodriguez
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Melissa Valenzuela
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Rong Lu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| |
Collapse
|
36
|
Moore KM, Pelletier AN, Lapp S, Metz A, Tharp GK, Lee M, Bhasin SS, Bhasin M, Sékaly RP, Bosinger SE, Suthar MS. Single cell analysis reveals an antiviral network that controls Zika virus infection in human dendritic cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.19.576293. [PMID: 38293140 PMCID: PMC10827181 DOI: 10.1101/2024.01.19.576293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Zika virus (ZIKV) is a mosquito-borne flavivirus that caused an epidemic in the Americas in 2016 and is linked to severe neonatal birth defects, including microcephaly and spontaneous abortion. To better understand the host response to ZIKV infection, we adapted the 10x Genomics Chromium single cell RNA sequencing (scRNA-seq) assay to simultaneously capture viral RNA and host mRNA. Using this assay, we profiled the antiviral landscape in a population of human moDCs infected with ZIKV at the single cell level. The bystander cells, which lacked detectable viral RNA, expressed an antiviral state that was enriched for genes coinciding predominantly with a type I interferon (IFN) response. Within the infected cells, viral RNA negatively correlated with type I IFN dependent and independent genes (antiviral module). We modeled the ZIKV specific antiviral state at the protein level leveraging experimentally derived protein-interaction data. We identified a highly interconnected network between the antiviral module and other host proteins. In this work, we propose a new paradigm for evaluating the antiviral response to a specific virus, combining an unbiased list of genes that highly correlate with viral RNA on a per cell basis with experimental protein interaction data. Our ZIKV-inclusive scRNA-seq assay will serve as a useful tool to gaining greater insight into the host response to ZIKV and can be applied more broadly to the flavivirus field.
Collapse
|
37
|
Swaminath S, Russell AB. The use of single-cell RNA-seq to study heterogeneity at varying levels of virus-host interactions. PLoS Pathog 2024; 20:e1011898. [PMID: 38236826 PMCID: PMC10796064 DOI: 10.1371/journal.ppat.1011898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2024] Open
Abstract
The outcome of viral infection depends on the diversity of the infecting viral population and the heterogeneity of the cell population that is infected. Until almost a decade ago, the study of these dynamic processes during viral infection was challenging and limited to certain targeted measurements. Presently, with the use of single-cell sequencing technology, the complex interface defined by the interactions of cells with infecting virus can now be studied across the breadth of the transcriptome in thousands of individual cells simultaneously. In this review, we will describe the use of single-cell RNA sequencing (scRNA-seq) to study the heterogeneity of viral infections, ranging from individual virions to the immune response between infected individuals. In addition, we highlight certain key experimental limitations and methodological decisions that are critical to analyzing scRNA-seq data at each scale.
Collapse
Affiliation(s)
- Sharmada Swaminath
- School of Biological Sciences, University of California, San Diego, La Jolla, California, United States of America
| | - Alistair B. Russell
- School of Biological Sciences, University of California, San Diego, La Jolla, California, United States of America
| |
Collapse
|
38
|
Hsu SCJ, Luu TU, Smith TD, Liu WF. Macro- and micro-scale culture environment differentially regulate the effects of crowding on macrophage function. Biotechnol Bioeng 2024; 121:306-316. [PMID: 37792882 DOI: 10.1002/bit.28554] [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] [Received: 08/23/2022] [Revised: 02/25/2023] [Accepted: 08/21/2023] [Indexed: 10/06/2023]
Abstract
Macrophages hold vital roles in immune defense, wound healing, and tissue homeostasis, and have the exquisite ability to sense and respond to dynamically changing cues in their microenvironment. Much of our understanding of their behavior has been derived from studies performed using in vitro culture systems, in which the cell environment can be precisely controlled. Recent advances in miniaturized culture platforms also offer the ability to recapitulate some features of the in vivo environment and analyze cellular responses at the single-cell level. Since macrophages are sensitive to their surrounding environments, the specific conditions in both macro- and micro-scale cultures likely contribute to observed responses. In this study, we investigate how the presence of neighboring cells influence macrophage activation following proinflammatory stimulation in both bulk and micro-scale culture. We found that in bulk cultures, higher seeding density negatively regulated the average TNF-α secretion from individual macrophages in response to inflammatory agonists, and this effect was partially caused by the reduced cell-to-media volume ratio. In contrast, studies conducted using microwells to isolate single cells and groups of cells revealed that increasing numbers of cells positively influences their inflammatory activation, suggesting that the absolute cell numbers in the system may be important. In addition, a single inflammatory cell enhanced the inflammatory state of a small group of cells. Overall, this work helps to better understand how variations of macroscopic and microscopic culture environments influence studies in macrophage biology and provides insight into how the presence of neighboring cells and the soluble environment influences macrophage activation.
Collapse
Affiliation(s)
- Ssu-Chieh J Hsu
- Department of Biomedical Engineering, University of California, Irvine, California, USA
- UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, University of California, Irvine, California, USA
| | - Thuy U Luu
- Department of Biomedical Engineering, University of California, Irvine, California, USA
- UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, University of California, Irvine, California, USA
| | - Tim D Smith
- Department of Biomedical Engineering, University of California, Irvine, California, USA
- UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, University of California, Irvine, California, USA
| | - Wendy F Liu
- Department of Biomedical Engineering, University of California, Irvine, California, USA
- UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, University of California, Irvine, California, USA
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, California, USA
- Institute for Immunology, University of California, Irvine, California, USA
- Department of Molecular Biology and Biochemistry, University of California, Irvine, California, USA
| |
Collapse
|
39
|
Wang P, Wen X, Li H, Lang P, Li S, Lei Y, Shu H, Gao L, Zhao D, Zeng J. Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON. Nat Commun 2023; 14:8459. [PMID: 38123534 PMCID: PMC10733330 DOI: 10.1038/s41467-023-44103-3] [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] [Received: 02/07/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.
Collapse
Affiliation(s)
- Peizhuo Wang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Xiao Wen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Peng Lang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Shuya Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Yipin Lei
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, 710071, Xi'an, Shaanxi Province, China
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China.
| |
Collapse
|
40
|
Uhlen M, Quake SR. Sequential sequencing by synthesis and the next-generation sequencing revolution. Trends Biotechnol 2023; 41:1565-1572. [PMID: 37482467 DOI: 10.1016/j.tibtech.2023.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/11/2023] [Accepted: 06/15/2023] [Indexed: 07/25/2023]
Abstract
The impact of next-generation sequencing (NGS) cannot be overestimated. The technology has transformed the field of life science, contributing to a dramatic expansion in our understanding of human health and disease and our understanding of biology and ecology. The vast majority of the major NGS systems today are based on the concept of 'sequencing by synthesis' (SBS) with sequential detection of nucleotide incorporation using an engineered DNA polymerase. Based on this strategy, various alternative platforms have been developed, including the use of either native nucleotides or reversible terminators and different strategies for the attachment of DNA to a solid support. In this review, some of the key concepts leading to this remarkable development are discussed.
Collapse
Affiliation(s)
- Mathias Uhlen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
| | - Stephen R Quake
- Departments of Bioengineering and Applied Physics, Stanford University, Stanford, CA, USA; Chan Zuckerberg Initiative, Redwood City, California, USA, Stanford, CA, USA
| |
Collapse
|
41
|
Zheng R, Xu Z, Zeng Y, Wang E, Li M. SPIDE: A single cell potency inference method based on the local cell-specific network entropy. Methods 2023; 220:90-97. [PMID: 37952704 DOI: 10.1016/j.ymeth.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 10/25/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023] Open
Abstract
For a given single cell RNA-seq data, it is critical to pinpoint key cellular stages and quantify cells' differentiation potency along a differentiation pathway in a time course manner. Currently, several methods based on the entropy of gene functions or PPI network have been proposed to solve the problem. Nevertheless, these methods still suffer from the inaccurate interactions and noises originating from scRNA-seq profile. In this study, we proposed a cell potency inference method based on cell-specific network entropy, called SPIDE. SPIDE introduces the local weighted cell-specific network for each cell to maintain cell heterogeneity and calculates the entropy by incorporating gene expression with network structure. In this study, we compared three cell entropy estimation models on eight scRNA-Seq datasets. The results show that SPIDE obtains consistent conclusions with real cell differentiation potency on most datasets. Moreover, SPIDE accurately recovers the continuous changes of potency during cell differentiation and significantly correlates with the stemness of tumor cells in Colorectal cancer. To conclude, our study provides a universal and accurate framework for cell entropy estimation, which deepens our understanding of cell differentiation, the development of diseases and other related biological research.
Collapse
Affiliation(s)
- Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Ziwei Xu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Yanping Zeng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Edwin Wang
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary T2N 4N1, Alberta, Canada
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
| |
Collapse
|
42
|
Gu Y, Hu Y, Zhang H, Wang S, Xu K, Su J. Single-cell RNA sequencing in osteoarthritis. Cell Prolif 2023; 56:e13517. [PMID: 37317049 PMCID: PMC10693192 DOI: 10.1111/cpr.13517] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/30/2023] [Accepted: 05/26/2023] [Indexed: 06/16/2023] Open
Abstract
Osteoarthritis is a progressive and heterogeneous joint disease with complex pathogenesis. The various phenotypes associated with each patient suggest that better subgrouping of tissues associated with genotypes in different phases of osteoarthritis may provide new insights into the onset and progression of the disease. Recently, single-cell RNA sequencing was used to describe osteoarthritis pathogenesis on a high-resolution view surpassing traditional technologies. Herein, this review summarizes the microstructural changes in articular cartilage, meniscus, synovium and subchondral bone that are mainly due to crosstalk amongst chondrocytes, osteoblasts, fibroblasts and endothelial cells during osteoarthritis progression. Next, we focus on the promising targets discovered by single-cell RNA sequencing and its potential applications in target drugs and tissue engineering. Additionally, the limited amount of research on the evaluation of bone-related biomaterials is reviewed. Based on the pre-clinical findings, we elaborate on the potential clinical values of single-cell RNA sequencing for the therapeutic strategies of osteoarthritis. Finally, a perspective on the future development of patient-centred medicine for osteoarthritis therapy combining other single-cell multi-omics technologies is discussed. This review will provide new insights into osteoarthritis pathogenesis on a cellular level and the field of applications of single-cell RNA sequencing in personalized therapeutics for osteoarthritis in the future.
Collapse
Affiliation(s)
- Yuyuan Gu
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
- School of MedicineShanghai UniversityShanghaiChina
| | - Yan Hu
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
| | - Hao Zhang
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
| | - Sicheng Wang
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
- Department of OrthopedicsShanghai Zhongye HospitalShanghaiChina
| | - Ke Xu
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
- Wenzhou Institute of Shanghai UniversityWenzhouChina
| | - Jiacan Su
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
| |
Collapse
|
43
|
Hume DA, Millard SM, Pettit AR. Macrophage heterogeneity in the single-cell era: facts and artifacts. Blood 2023; 142:1339-1347. [PMID: 37595274 DOI: 10.1182/blood.2023020597] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/09/2023] [Accepted: 08/09/2023] [Indexed: 08/20/2023] Open
Abstract
In this spotlight, we review technical issues that compromise single-cell analysis of tissue macrophages, including limited and unrepresentative yields, fragmentation and generation of remnants, and activation during tissue disaggregation. These issues may lead to a misleading definition of subpopulations of macrophages and the expression of macrophage-specific transcripts by unrelated cells. Recognition of the technical limitations of single-cell approaches is required in order to map the full spectrum of tissue-resident macrophage heterogeneity and assess its biological significance.
Collapse
Affiliation(s)
- David A Hume
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, QLD, Australia
| | - Susan M Millard
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, QLD, Australia
| | - Allison R Pettit
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, QLD, Australia
| |
Collapse
|
44
|
Naderi-Meshkin H, Cornelius VA, Eleftheriadou M, Potel KN, Setyaningsih WAW, Margariti A. Vascular organoids: unveiling advantages, applications, challenges, and disease modelling strategies. Stem Cell Res Ther 2023; 14:292. [PMID: 37817281 PMCID: PMC10566155 DOI: 10.1186/s13287-023-03521-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/26/2023] [Indexed: 10/12/2023] Open
Abstract
Understanding mechanisms and manifestations of cardiovascular risk factors, including diabetes, on vascular cells such as endothelial cells, pericytes, and vascular smooth muscle cells, remains elusive partly due to the lack of appropriate disease models. Therefore, here we explore different aspects for the development of advanced 3D in vitro disease models that recapitulate human blood vessel complications using patient-derived induced pluripotent stem cells, which retain the epigenetic, transcriptomic, and metabolic memory of their patient-of-origin. In this review, we highlight the superiority of 3D blood vessel organoids over conventional 2D cell culture systems for vascular research. We outline the key benefits of vascular organoids in both health and disease contexts and discuss the current challenges associated with organoid technology, providing potential solutions. Furthermore, we discuss the diverse applications of vascular organoids and emphasize the importance of incorporating all relevant cellular components in a 3D model to accurately recapitulate vascular pathophysiology. As a specific example, we present a comprehensive overview of diabetic vasculopathy, demonstrating how the interplay of different vascular cell types is critical for the successful modelling of complex disease processes in vitro. Finally, we propose a strategy for creating an organ-specific diabetic vasculopathy model, serving as a valuable template for modelling other types of vascular complications in cardiovascular diseases by incorporating disease-specific stressors and organotypic modifications.
Collapse
Affiliation(s)
- Hojjat Naderi-Meshkin
- The Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Victoria A Cornelius
- The Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Magdalini Eleftheriadou
- The Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Koray Niels Potel
- The Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Wiwit Ananda Wahyu Setyaningsih
- The Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7BL, UK
- Department of Anatomy, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Sleman, D.I. Yogyakarta, 55281, Indonesia
| | - Andriana Margariti
- The Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7BL, UK.
| |
Collapse
|
45
|
Naigles B, Narla AV, Soroczynski J, Tsimring LS, Hao N. Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells. J Biol Chem 2023; 299:105230. [PMID: 37689116 PMCID: PMC10579967 DOI: 10.1016/j.jbc.2023.105230] [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] [Received: 05/12/2023] [Revised: 09/02/2023] [Accepted: 09/03/2023] [Indexed: 09/11/2023] Open
Abstract
Macrophages must respond appropriately to pathogens and other pro-inflammatory stimuli in order to perform their roles in fighting infection. One way in which inflammatory stimuli can vary is in their dynamics-that is, the amplitude and duration of stimulus experienced by the cell. In this study, we performed long-term live cell imaging in a microfluidic device to investigate how the pro-inflammatory genes IRF1, CXCL10, and CXCL9 respond to dynamic interferon-gamma (IFNγ) stimulation. We found that IRF1 responds to low concentration or short duration IFNγ stimulation, whereas CXCL10 and CXCL9 require longer or higherconcentration stimulation to be expressed. We also investigated the heterogeneity in the expression of each gene and found that CXCL10 and CXCL9 have substantial cell-to-cell variability. In particular, the expression of CXCL10 appears to be largely stochastic with a subpopulation of nonresponding cells across all the stimulation conditions tested. We developed both deterministic and stochastic models for the expression of each gene. Our modeling analysis revealed that the heterogeneity in CXCL10 can be attributed to a slow chromatin-opening step that is on a similar timescale to that of adaptation of the upstream signal. In this way, CXCL10 expression in individual cells can remain stochastic in response to each pulse of repeated stimulation, which we also validated by experiments. Together, we conclude that pro-inflammatory genes in the same signaling pathway can respond to dynamic IFNγ stimulus with very different response features and that upstream signal adaptation can contribute to shaping heterogeneous gene expression.
Collapse
Affiliation(s)
- Beverly Naigles
- Department of Molecular Biology, University of California San Diego, La Jolla, California, USA
| | - Avaneesh V Narla
- Department of Physics, University of California San Diego, La Jolla, California, USA
| | - Jan Soroczynski
- Laboratory of Genome Architecture and Dynamics, The Rockefeller University, New York, New York, USA
| | - Lev S Tsimring
- Synthetic Biology Institute, University of California San Diego, La Jolla, California, USA
| | - Nan Hao
- Department of Molecular Biology, University of California San Diego, La Jolla, California, USA; Synthetic Biology Institute, University of California San Diego, La Jolla, California, USA; Department of Bioengineering, University of California San Diego, La Jolla, California, USA.
| |
Collapse
|
46
|
Mao G, Pang Z, Zuo K, Wang Q, Pei X, Chen X, Liu J. Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks. Brief Bioinform 2023; 24:bbad414. [PMID: 37985457 PMCID: PMC10661972 DOI: 10.1093/bib/bbad414] [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] [Received: 08/03/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/22/2023] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for studying gene expression patterns at the single-cell level. Inferring gene regulatory networks (GRNs) from scRNA-seq data provides insight into cellular phenotypes from the genomic level. However, the high sparsity, noise and dropout events inherent in scRNA-seq data present challenges for GRN inference. In recent years, the dramatic increase in data on experimentally validated transcription factors binding to DNA has made it possible to infer GRNs by supervised methods. In this study, we address the problem of GRN inference by framing it as a graph link prediction task. In this paper, we propose a novel framework called GNNLink, which leverages known GRNs to deduce the potential regulatory interdependencies between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce a graph convolutional network-based interaction graph encoder to effectively refine gene features by capturing interdependencies between nodes in the network. Finally, the inference of GRN is obtained by performing matrix completion operation on node features. The features obtained from model training can be applied to downstream tasks such as measuring similarity and inferring causality between gene pairs. To evaluate the performance of GNNLink, we compare it with six existing GRN reconstruction methods using seven scRNA-seq datasets. These datasets encompass diverse ground truth networks, including functional interaction networks, Loss of Function/Gain of Function data, non-specific ChIP-seq data and cell-type-specific ChIP-seq data. Our experimental results demonstrate that GNNLink achieves comparable or superior performance across these datasets, showcasing its robustness and accuracy. Furthermore, we observe consistent performance across datasets of varying scales. For reproducibility, we provide the data and source code of GNNLink on our GitHub repository: https://github.com/sdesignates/GNNLink.
Collapse
Affiliation(s)
- Guo Mao
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Zhengbin Pang
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Ke Zuo
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Qinglin Wang
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Xiangdong Pei
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Xinhai Chen
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Jie Liu
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
- Laboratory of Software Engineering for Complex System, National University of Defense Technology, deya, 410073 Changsha, China
| |
Collapse
|
47
|
Shojaee A, Huang SSC. Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions. Brief Bioinform 2023; 24:bbad370. [PMID: 37897702 PMCID: PMC10612495 DOI: 10.1093/bib/bbad370] [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: 06/18/2023] [Revised: 09/06/2023] [Accepted: 09/29/2023] [Indexed: 10/30/2023] Open
Abstract
Gene regulatory networks (GRNs) drive organism structure and functions, so the discovery and characterization of GRNs is a major goal in biological research. However, accurate identification of causal regulatory connections and inference of GRNs using gene expression datasets, more recently from single-cell RNA-seq (scRNA-seq), has been challenging. Here we employ the innovative method of Causal Inference Using Composition of Transactions (CICT) to uncover GRNs from scRNA-seq data. The basis of CICT is that if all gene expressions were random, a non-random regulatory gene should induce its targets at levels different from the background random process, resulting in distinct patterns in the whole relevance network of gene-gene associations. CICT proposes novel network features derived from a relevance network, which enable any machine learning algorithm to predict causal regulatory edges and infer GRNs. We evaluated CICT using simulated and experimental scRNA-seq data in a well-established benchmarking pipeline and showed that CICT outperformed existing network inference methods representing diverse approaches with many-fold higher accuracy. Furthermore, we demonstrated that GRN inference with CICT was robust to different levels of sparsity in scRNA-seq data, the characteristics of data and ground truth, the choice of association measure and the complexity of the supervised machine learning algorithm. Our results suggest aiming at directly predicting causality to recover regulatory relationships in complex biological networks substantially improves accuracy in GRN inference.
Collapse
Affiliation(s)
- Abbas Shojaee
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| | - Shao-shan Carol Huang
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| |
Collapse
|
48
|
Zhao J, Wong CW, Ching WK, Cheng X. NG-SEM: an effective non-Gaussian structural equation modeling framework for gene regulatory network inference from single-cell RNA-seq data. Brief Bioinform 2023; 24:bbad369. [PMID: 37864293 DOI: 10.1093/bib/bbad369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/25/2023] [Accepted: 09/29/2023] [Indexed: 10/22/2023] Open
Abstract
Inference of gene regulatory network (GRN) from gene expression profiles has been a central problem in systems biology and bioinformatics in the past decades. The tremendous emergency of single-cell RNA sequencing (scRNA-seq) data brings new opportunities and challenges for GRN inference: the extensive dropouts and complicated noise structure may also degrade the performance of contemporary gene regulatory models. Thus, there is an urgent need to develop more accurate methods for gene regulatory network inference in single-cell data while considering the noise structure at the same time. In this paper, we extend the traditional structural equation modeling (SEM) framework by considering a flexible noise modeling strategy, namely we use the Gaussian mixtures to approximate the complex stochastic nature of a biological system, since the Gaussian mixture framework can be arguably served as a universal approximation for any continuous distributions. The proposed non-Gaussian SEM framework is called NG-SEM, which can be optimized by iteratively performing Expectation-Maximization algorithm and weighted least-squares method. Moreover, the Akaike Information Criteria is adopted to select the number of components of the Gaussian mixture. To probe the accuracy and stability of our proposed method, we design a comprehensive variate of control experiments to systematically investigate the performance of NG-SEM under various conditions, including simulations and real biological data sets. Results on synthetic data demonstrate that this strategy can improve the performance of traditional Gaussian SEM model and results on real biological data sets verify that NG-SEM outperforms other five state-of-the-art methods.
Collapse
Affiliation(s)
- Jiaying Zhao
- Department of Mathematics, The University of Hongkong, Pokfulam road, Hong Kong
| | - Chi-Wing Wong
- Department of Mathematics, The University of Hongkong, Pokfulam road, Hong Kong
| | - Wai-Ki Ching
- Department of Mathematics, The University of Hongkong, Pokfulam road, Hong Kong
| | - Xiaoqing Cheng
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, ShaanXi, China
| |
Collapse
|
49
|
Hausmann F, Ergen C, Khatri R, Marouf M, Hänzelmann S, Gagliani N, Huber S, Machart P, Bonn S. DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection. Genome Biol 2023; 24:212. [PMID: 37730638 PMCID: PMC10510283 DOI: 10.1186/s13059-023-03049-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 08/23/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Single-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification. RESULTS Here, we present DISCERN, a novel deep generative network that precisely reconstructs missing single-cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We show that DISCERN is robust against differences between batches and is able to keep biological differences between batches, which is a common problem for imputation and batch correction algorithms. We use DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4+ and CD8+ Tc2 T helper cells, with a potential role in adverse disease outcome. We utilize T cell fraction information of patient blood to classify mild or severe COVID-19 with an AUROC of 80% that can serve as a biomarker of disease stage. DISCERN can be easily integrated into existing single-cell sequencing workflow. CONCLUSIONS Thus, DISCERN is a flexible tool for reconstructing missing single-cell gene expression using a reference dataset and can easily be applied to a variety of data sets yielding novel insights, e.g., into disease mechanisms.
Collapse
Affiliation(s)
- Fabian Hausmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Can Ergen
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Robin Khatri
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Mohamed Marouf
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Sonja Hänzelmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Nicola Gagliani
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- Section of Molecular Immunology und Gastroenterology, I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Samuel Huber
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Pierre Machart
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Stefan Bonn
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany.
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany.
- Hamburg Center for Translational Immunology (HCTI), I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany.
| |
Collapse
|
50
|
Wang W, Zhou X, Wang J, Yao J, Wen H, Wang Y, Sun M, Zhang C, Tao W, Zou J, Ni T. Approximate estimation of cell-type resolution transcriptome in bulk tissue through matrix completion. Brief Bioinform 2023; 24:bbad273. [PMID: 37529921 DOI: 10.1093/bib/bbad273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/20/2023] [Accepted: 07/10/2023] [Indexed: 08/03/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for uncovering cellular heterogeneity. However, the high costs associated with this technique have rendered it impractical for studying large patient cohorts. We introduce ENIGMA (Deconvolution based on Regularized Matrix Completion), a method that addresses this limitation through accurately deconvoluting bulk tissue RNA-seq data into a readout with cell-type resolution by leveraging information from scRNA-seq data. By employing a matrix completion strategy, ENIGMA minimizes the distance between the mixture transcriptome obtained with bulk sequencing and a weighted combination of cell-type-specific expression. This allows the quantification of cell-type proportions and reconstruction of cell-type-specific transcriptomes. To validate its performance, ENIGMA was tested on both simulated and real datasets, including disease-related tissues, demonstrating its ability in uncovering novel biological insights.
Collapse
Affiliation(s)
- Weixu Wang
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Xiaolan Zhou
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Jing Wang
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Jun Yao
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Haimei Wen
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Yi Wang
- Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Mingwan Sun
- Key Laboratory of Gene Engineering of the Ministry of Education and State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510006, P.R. China
| | - Chao Zhang
- MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, P.R. China
| | - Wei Tao
- MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, P.R. China
| | - Jiahua Zou
- Guangdong Provincial Key Laboratory of Bioengineering Medicine, National Engineering Research Center of Genetic Medicine, Institute of Biomedicine, College of Life Science and Technology, Jinan University, Guangzhou 510632, P.R. China
| | - Ting Ni
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, P.R. China
| |
Collapse
|