1
|
Zou LS, Cable DM, Barrera-Lopez IA, Zhao T, Murray E, Aryee MJ, Chen F, Irizarry RA. Detection of allele-specific expression in spatial transcriptomics with spASE. Genome Biol 2024; 25:180. [PMID: 38978101 PMCID: PMC11229351 DOI: 10.1186/s13059-024-03317-4] [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: 11/02/2023] [Accepted: 06/20/2024] [Indexed: 07/10/2024] Open
Abstract
Spatial transcriptomics technologies permit the study of the spatial distribution of RNA at near-single-cell resolution genome-wide. However, the feasibility of studying spatial allele-specific expression (ASE) from these data remains uncharacterized. Here, we introduce spASE, a computational framework for detecting and estimating spatial ASE. To tackle the challenges presented by cell type mixtures and a low signal to noise ratio, we implement a hierarchical model involving additive mixtures of spatial smoothing splines. We apply our method to allele-resolved Visium and Slide-seq from the mouse cerebellum and hippocampus and report new insight into the landscape of spatial and cell type-specific ASE therein.
Collapse
Affiliation(s)
- Luli S Zou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Dylan M Cable
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, 02139, USA
| | | | - Tongtong Zhao
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Evan Murray
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Martin J Aryee
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Rafael A Irizarry
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
| |
Collapse
|
2
|
Sun Y, Wiese M, Hmadi R, Karayol R, Seyfferth J, Martinez Greene JA, Erdogdu NU, Deboutte W, Arrigoni L, Holz H, Renschler G, Hirsch N, Foertsch A, Basilicata MF, Stehle T, Shvedunova M, Bella C, Pessoa Rodrigues C, Schwalb B, Cramer P, Manke T, Akhtar A. MSL2 ensures biallelic gene expression in mammals. Nature 2023; 624:173-181. [PMID: 38030723 PMCID: PMC10700137 DOI: 10.1038/s41586-023-06781-3] [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: 05/29/2022] [Accepted: 10/24/2023] [Indexed: 12/01/2023]
Abstract
In diploid organisms, biallelic gene expression enables the production of adequate levels of mRNA1,2. This is essential for haploinsufficient genes, which require biallelic expression for optimal function to prevent the onset of developmental disorders1,3. Whether and how a biallelic or monoallelic state is determined in a cell-type-specific manner at individual loci remains unclear. MSL2 is known for dosage compensation of the male X chromosome in flies. Here we identify a role of MSL2 in regulating allelic expression in mammals. Allele-specific bulk and single-cell analyses in mouse neural progenitor cells revealed that, in addition to the targets showing biallelic downregulation, a class of genes transitions from biallelic to monoallelic expression after MSL2 loss. Many of these genes are haploinsufficient. In the absence of MSL2, one allele remains active, retaining active histone modifications and transcription factor binding, whereas the other allele is silenced, exhibiting loss of promoter-enhancer contacts and the acquisition of DNA methylation. Msl2-knockout mice show perinatal lethality and heterogeneous phenotypes during embryonic development, supporting a role for MSL2 in regulating gene dosage. The role of MSL2 in preserving biallelic expression of specific dosage-sensitive genes sets the stage for further investigation of other factors that are involved in allelic dosage compensation in mammalian cells, with considerable implications for human disease.
Collapse
Affiliation(s)
- Yidan Sun
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Meike Wiese
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Raed Hmadi
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Remzi Karayol
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Janine Seyfferth
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Juan Alfonso Martinez Greene
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Niyazi Umut Erdogdu
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Ward Deboutte
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Laura Arrigoni
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Herbert Holz
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Gina Renschler
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Naama Hirsch
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Arion Foertsch
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | | | - Thomas Stehle
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Maria Shvedunova
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Chiara Bella
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | | | - Bjoern Schwalb
- Max Planck Institute for Multidisciplinary Sciences, Goettingen, Germany
| | - Patrick Cramer
- Max Planck Institute for Multidisciplinary Sciences, Goettingen, Germany
| | - Thomas Manke
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Asifa Akhtar
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany.
| |
Collapse
|
3
|
Bresnahan ST, Lee E, Clark L, Ma R, Rangel J, Grozinger CM, Li-Byarlay H. Examining parent-of-origin effects on transcription and RNA methylation in mediating aggressive behavior in honey bees (Apis mellifera). BMC Genomics 2023; 24:315. [PMID: 37308882 DOI: 10.1186/s12864-023-09411-4] [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: 02/23/2023] [Accepted: 05/27/2023] [Indexed: 06/14/2023] Open
Abstract
Conflict between genes inherited from the mother (matrigenes) and the father (patrigenes) is predicted to arise during social interactions among offspring if these genes are not evenly distributed among offspring genotypes. This intragenomic conflict drives parent-specific transcription patterns in offspring resulting from parent-specific epigenetic modifications. Previous tests of the kinship theory of intragenomic conflict in honey bees (Apis mellifera) provided evidence in support of theoretical predictions for variation in worker reproduction, which is associated with extreme variation in morphology and behavior. However, more subtle behaviors - such as aggression - have not been extensively studied. Additionally, the canonical epigenetic mark (DNA methylation) associated with parent-specific transcription in plant and mammalian model species does not appear to play the same role as in honey bees, and thus the molecular mechanisms underlying intragenomic conflict in this species is an open area of investigation. Here, we examined the role of intragenomic conflict in shaping aggression in honey bee workers through a reciprocal cross design and Oxford Nanopore direct RNA sequencing. We attempted to probe the underlying regulatory basis of this conflict through analyses of parent-specific RNA m6A and alternative splicing patterns. We report evidence that intragenomic conflict occurs in the context of honey bee aggression, with increased paternal and maternal allele-biased transcription in aggressive compared to non-aggressive bees, and higher paternal allele-biased transcription overall. However, we found no evidence to suggest that RNA m6A or alternative splicing mediate intragenomic conflict in this species.
Collapse
Affiliation(s)
- Sean T Bresnahan
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, USA.
| | - Ellen Lee
- Agricultural Research and Development Program, Central State University, Wilberforce, USA
- Department of Biological Sciences, Wright State University, Dayton, USA
| | - Lindsay Clark
- HPCBio, University of Illinois at Urbana-Champaign, Champaign, USA
- Research Scientific Computing Group, Seattle Children's Research Institute, Seattle, USA
| | - Rong Ma
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, USA
| | - Juliana Rangel
- Department of Entomology, Texas A&M University, College Station, USA
| | - Christina M Grozinger
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, USA
| | - Hongmei Li-Byarlay
- Agricultural Research and Development Program, Central State University, Wilberforce, USA.
- Department of Agricultural and Life Science, Central State University, Wilberforce, USA.
| |
Collapse
|
4
|
Kravitz SN, Ferris E, Love MI, Thomas A, Quinlan AR, Gregg C. Random allelic expression in the adult human body. Cell Rep 2023; 42:111945. [PMID: 36640362 PMCID: PMC10484211 DOI: 10.1016/j.celrep.2022.111945] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 10/17/2022] [Accepted: 12/15/2022] [Indexed: 01/07/2023] Open
Abstract
Genes are typically assumed to express both parental alleles similarly, yet cell lines show random allelic expression (RAE) for many autosomal genes that could shape genetic effects. Thus, understanding RAE in human tissues could improve our understanding of phenotypic variation. Here, we develop a methodology to perform genome-wide profiling of RAE and biallelic expression in GTEx datasets for 832 people and 54 tissues. We report 2,762 autosomal genes with some RAE properties similar to randomly inactivated X-linked genes. We found that RAE is associated with rapidly evolving regions in the human genome, adaptive signaling processes, and genes linked to age-related diseases such as neurodegeneration and cancer. We define putative mechanistic subtypes of RAE distinguished by gene overlaps on sense and antisense DNA strands, aggregation in clusters near telomeres, and increased regulatory complexity and inputs compared with biallelic genes. We provide foundations to study RAE in human phenotypes, evolution, and disease.
Collapse
Affiliation(s)
- Stephanie N Kravitz
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA; Neurobiology, University of Utah, Salt Lake City, UT, USA
| | - Elliott Ferris
- Neurobiology, University of Utah, Salt Lake City, UT, USA
| | - Michael I Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alun Thomas
- Department of Internal Medicine, Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Aaron R Quinlan
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Christopher Gregg
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA; Neurobiology, University of Utah, Salt Lake City, UT, USA.
| |
Collapse
|
5
|
Edwards N, Dillard C, Prashant NM, Hongyu L, Yang M, Ulianova E, Horvath A. SCExecute: custom cell barcode-stratified analyses of scRNA-seq data. Bioinformatics 2022; 39:6854977. [PMID: 36448703 PMCID: PMC9825775 DOI: 10.1093/bioinformatics/btac768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 11/11/2022] [Accepted: 11/29/2022] [Indexed: 12/05/2022] Open
Abstract
MOTIVATION In single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not sufficiently supported by available tools designed for high-throughput sequencing data. RESULTS We introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers-GATK and Strelka2-executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System).In conclusion, SCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features. AVAILABILITY AND IMPLEMENTATION SCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | - Christian Dillard
- Department of Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA
| | - N M Prashant
- Department of Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA,Departments of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Liu Hongyu
- Department of Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA,Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA
| | - Mia Yang
- Department of Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA
| | - Evgenia Ulianova
- Department of Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA
| | | |
Collapse
|
6
|
Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease. Genes (Basel) 2022; 13:genes13050764. [PMID: 35627149 PMCID: PMC9141211 DOI: 10.3390/genes13050764] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/04/2022] [Accepted: 04/13/2022] [Indexed: 02/04/2023] Open
Abstract
The early developmental phase is of critical importance for human health and disease later in life. To decipher the molecular mechanisms at play, current biomedical research is increasingly relying on large quantities of diverse omics data. The integration and interpretation of the different datasets pose a critical challenge towards the holistic understanding of the complex biological processes that are involved in early development. In this review, we outline the major transcriptomic and epigenetic processes and the respective datasets that are most relevant for studying the periconceptional period. We cover both basic data processing and analysis steps, as well as more advanced data integration methods. A particular focus is given to network-based methods. Finally, we review the medical applications of such integrative analyses.
Collapse
|
7
|
Sands B, Yun S, Mendenhall AR. Introns control stochastic allele expression bias. Nat Commun 2021; 12:6527. [PMID: 34764277 PMCID: PMC8585970 DOI: 10.1038/s41467-021-26798-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 10/19/2021] [Indexed: 01/26/2023] Open
Abstract
Monoallelic expression (MAE) or extreme allele bias can account for incomplete penetrance, missing heritability and non-Mendelian diseases. In cancer, MAE is associated with shorter patient survival times and higher tumor grade. Prior studies showed that stochastic MAE is caused by stochastic epigenetic silencing, in a gene and tissue-specific manner. Here, we used C. elegans to study stochastic MAE in vivo. We found allele bias/MAE to be widespread within C. elegans tissues, presenting as a continuum from fully biallelic to MAE. We discovered that the presence of introns within alleles robustly decreases MAE. We determined that introns control MAE at distinct loci, in distinct cell types, with distinct promoters, and within distinct coding sequences, using a 5'-intron position-dependent mechanism. Bioinformatic analysis showed human intronless genes are significantly enriched for MAE. Our experimental evidence demonstrates a role for introns in regulating MAE, possibly explaining why some mutations within introns result in disease.
Collapse
Affiliation(s)
- Bryan Sands
- grid.34477.330000000122986657Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA USA
| | - Soo Yun
- grid.34477.330000000122986657Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA USA
| | - Alexander R. Mendenhall
- grid.34477.330000000122986657Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA USA
| |
Collapse
|
8
|
Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments. Genes (Basel) 2021; 12:genes12101558. [PMID: 34680953 PMCID: PMC8535975 DOI: 10.3390/genes12101558] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/25/2021] [Accepted: 09/28/2021] [Indexed: 11/17/2022] Open
Abstract
Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual cell scRNA-seq data, wherein the alignments are split by cellular barcode prior to the variant call. We also reanalyze publicly available data on the MCF7 cell line during anticancer treatment. We assessed SNV calls by three variant callers—GATK, Strelka2, and Mutect2, in combination with a method for the cell-level tabulation of the sequencing read counts bearing variant alleles–SCReadCounts (single-cell read counts). Our analysis shows that variant calls on individual cell alignments identify at least a two-fold higher number of SNVs as compared to the pooled scRNA-seq; these SNVs are enriched in novel variants and in stop-codon and missense substitutions. Our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes the need for cell-level variant detection approaches and tools, which can contribute to the understanding of the cellular heterogeneity and the relationships to phenotypes, and help elucidate somatic mutation evolution and functionality.
Collapse
|
9
|
Prashant NM, Alomran N, Chen Y, Liu H, Bousounis P, Movassagh M, Edwards N, Horvath A. SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data. BMC Genomics 2021; 22:689. [PMID: 34551708 PMCID: PMC8459565 DOI: 10.1186/s12864-021-07974-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 09/03/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. RESULTS To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available ( https://github.com/HorvathLab/NGS ) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source. CONCLUSIONS SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.
Collapse
Affiliation(s)
- N M Prashant
- McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC, 20037, USA
- Departments of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nawaf Alomran
- McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC, 20037, USA
| | - Yu Chen
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, DC, 20057, USA
| | - Hongyu Liu
- McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC, 20037, USA
| | - Pavlos Bousounis
- McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC, 20037, USA
| | - Mercedeh Movassagh
- Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA, USA
| | - Nathan Edwards
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, DC, 20057, USA
| | - Anelia Horvath
- McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC, 20037, USA.
| |
Collapse
|
10
|
Gupta RK, Kuznicki J. Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing. Cells 2020; 9:E1751. [PMID: 32707839 PMCID: PMC7463515 DOI: 10.3390/cells9081751] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/15/2020] [Accepted: 07/20/2020] [Indexed: 01/01/2023] Open
Abstract
The present review discusses recent progress in single-cell RNA sequencing (scRNA-seq), which can describe cellular heterogeneity in various organs, bodily fluids, and pathologies (e.g., cancer and Alzheimer's disease). We outline scRNA-seq techniques that are suitable for investigating cellular heterogeneity that is present in cell populations with very high resolution of the transcriptomic landscape. We summarize scRNA-seq findings and applications of this technology to identify cell types, activity, and other features that are important for the function of different bodily organs. We discuss future directions for scRNA-seq techniques that can link gene expression, protein expression, cellular function, and their roles in pathology. We speculate on how the field could develop beyond its present limitations (e.g., performing scRNA-seq in situ and in vivo). Finally, we discuss the integration of machine learning and artificial intelligence with cutting-edge scRNA-seq technology, which could provide a strong basis for designing precision medicine and targeted therapy in the future.
Collapse
Affiliation(s)
- Rishikesh Kumar Gupta
- International Institute of Molecular and Cell Biology in Warsaw, Trojdena 4, 02-109 Warsaw Poland;
- Postgraduate School of Molecular Medicine, Warsaw Medical University, 61 Żwirki i Wigury St., 02-091 Warsaw, Poland
| | - Jacek Kuznicki
- International Institute of Molecular and Cell Biology in Warsaw, Trojdena 4, 02-109 Warsaw Poland;
| |
Collapse
|
11
|
The Role of Single-Cell Technology in the Study and Control of Infectious Diseases. Cells 2020; 9:cells9061440. [PMID: 32531928 PMCID: PMC7348906 DOI: 10.3390/cells9061440] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 02/07/2023] Open
Abstract
The advent of single-cell research in the recent decade has allowed biological studies at an unprecedented resolution and scale. In particular, single-cell analysis techniques such as Next-Generation Sequencing (NGS) and Fluorescence-Activated Cell Sorting (FACS) have helped show substantial links between cellular heterogeneity and infectious disease progression. The extensive characterization of genomic and phenotypic biomarkers, in addition to host-pathogen interactions at the single-cell level, has resulted in the discovery of previously unknown infection mechanisms as well as potential treatment options. In this article, we review the various single-cell technologies and their applications in the ongoing fight against infectious diseases, as well as discuss the potential opportunities for future development.
Collapse
|