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1
Ochsner, Scott A., Christopher M. Watkins, Apollo McOwiti, Xueping Xu, Yolanda F. Darlington, Michael D. Dehart, Austin J. Cooney, David L. Steffen, Lauren B. Becnel, and Neil J. McKenna. "Transcriptomine, a web resource for nuclear receptor signaling transcriptomes." Physiological Genomics 44, no. 17 (September 1, 2012): 853–63. http://dx.doi.org/10.1152/physiolgenomics.00033.2012.
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The nuclear receptor (NR) superfamily of ligand-regulated transcription factors directs ligand- and tissue-specific transcriptomes in myriad developmental, metabolic, immunological, and reproductive processes. The NR signaling field has generated a wealth of genome-wide expression data points, but due to deficits in their accessibility, annotation, and integration, the full potential of these studies has not yet been realized. We searched public gene expression databases and MEDLINE for global transcriptomic datasets relevant to NRs, their ligands, and coregulators. We carried out extensive, deep reannotation of the datasets using controlled vocabularies for RNA Source and regulating molecule and resolved disparate gene identifiers to official gene symbols to facilitate comparison of fold changes and their significance across multiple datasets. We assembled these data points into a database, Transcriptomine ( http://www.nursa.org/transcriptomine ), that allows for multiple, menu-driven querying strategies of this transcriptomic “superdataset,” including single and multiple genes, Gene Ontology terms, disease terms, and uploaded custom gene lists. Experimental variables such as regulating molecule, RNA Source, as well as fold-change and P value cutoff values can be modified, and full data records can be either browsed or downloaded for downstream analysis. We demonstrate the utility of Transcriptomine as a hypothesis generation and validation tool using in silico and experimental use cases. Our resource empowers users to instantly and routinely mine the collective biology of millions of previously disparate transcriptomic data points. By incorporating future transcriptome-wide datasets in the NR signaling field, we anticipate Transcriptomine developing into a powerful resource for the NR- and other signal transduction research communities.
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Ali, Abdullah Mahmood, and Azra Raza. "scRNAseq and High-Throughput Spatial Analysis of Tumor and Normal Microenvironment in Solid Tumors Reveal a Possible Origin of Circulating Tumor Hybrid Cells." Cancers 16, no. 7 (April 8, 2024): 1444. http://dx.doi.org/10.3390/cancers16071444.
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Metastatic cancer is a leading cause of death in cancer patients worldwide. While circulating hybrid cells (CHCs) are implicated in metastatic spread, studies documenting their tissue origin remain sparse, with limited candidate approaches using one–two markers. Utilizing high-throughput single-cell and spatial transcriptomics, we identified tumor hybrid cells (THCs) co-expressing epithelial and macrophage markers and expressing a distinct transcriptome. Rarely, normal tissue showed these cells (NHCs), but their transcriptome was easily distinguishable from THCs. THCs with unique transcriptomes were observed in breast and colon cancers, suggesting this to be a generalizable phenomenon across cancer types. This study establishes a framework for HC identification in large datasets, providing compelling evidence for their tissue residence and offering comprehensive transcriptomic characterization. Furthermore, it sheds light on their differential function and identifies pathways that could explain their newly acquired invasive capabilities. THCs should be considered as potential therapeutic targets.
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Chen, Wanze, Orane Guillaume-Gentil, Pernille Yde Rainer, Christoph G. Gäbelein, Wouter Saelens, Vincent Gardeux, Amanda Klaeger, et al. "Live-seq enables temporal transcriptomic recording of single cells." Nature 608, no. 7924 (August 17, 2022): 733–40. http://dx.doi.org/10.1038/s41586-022-05046-9.
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AbstractSingle-cell transcriptomics (scRNA-seq) has greatly advanced our ability to characterize cellular heterogeneity1. However, scRNA-seq requires lysing cells, which impedes further molecular or functional analyses on the same cells. Here, we established Live-seq, a single-cell transcriptome profiling approach that preserves cell viability during RNA extraction using fluidic force microscopy2,3, thus allowing to couple a cell’s ground-state transcriptome to its downstream molecular or phenotypic behaviour. To benchmark Live-seq, we used cell growth, functional responses and whole-cell transcriptome read-outs to demonstrate that Live-seq can accurately stratify diverse cell types and states without inducing major cellular perturbations. As a proof of concept, we show that Live-seq can be used to directly map a cell’s trajectory by sequentially profiling the transcriptomes of individual macrophages before and after lipopolysaccharide (LPS) stimulation, and of adipose stromal cells pre- and post-differentiation. In addition, we demonstrate that Live-seq can function as a transcriptomic recorder by preregistering the transcriptomes of individual macrophages that were subsequently monitored by time-lapse imaging after LPS exposure. This enabled the unsupervised, genome-wide ranking of genes on the basis of their ability to affect macrophage LPS response heterogeneity, revealing basal Nfkbia expression level and cell cycle state as important phenotypic determinants, which we experimentally validated. Thus, Live-seq can address a broad range of biological questions by transforming scRNA-seq from an end-point to a temporal analysis approach.
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Callaway, Edward M., Hong-Wei Dong, Joseph R. Ecker, Michael J. Hawrylycz, Z. Josh Huang, Ed S. Lein, John Ngai, et al. "A multimodal cell census and atlas of the mammalian primary motor cortex." Nature 598, no. 7879 (October 6, 2021): 86–102. http://dx.doi.org/10.1038/s41586-021-03950-0.
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AbstractHere we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1–5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
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MA, Hoi Tang, Chun Yin YU, and Lau Yan NG. "Abstract 7102: The central dogma of hepatocellular carcinoma: Genomic, transcriptomic, and proteomic changes." Cancer Research 84, no. 6_Supplement (March 22, 2024): 7102. http://dx.doi.org/10.1158/1538-7445.am2024-7102.
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Abstract Cancer is a disease condition characterized by remarkable heterogeneity, wherein numerous molecular features display considerable disparities even with the same patient cohorts, leading to varied treatment outcomes. The primary factor contributing to the substantial differences observed across cancers stems from unique combinations of genetic mutations. Epigenetic alterations further contribute to differential gene expression and splicing, thereby enhancing transcriptomic diversity. The protein products originating from the cancer transcriptome constitute the specific intracellular and extracellular environment, consequently exerting supplementary effects on epigenetic, transcriptomic, and proteomic changes. The complex interplay and mutual influence between genetic aberrations, transcriptomes, and proteomes significantly contribute to shaping the extensive spectrum of molecular characteristics observed in cancer. With label-free mass spectrometry, RNA-sequencing, and low-pass whole genome sequencing, the quantification of the proteome, transcriptome, and genetic abbreviation of the cancer can be evaluated. With the mouse hydrodynamic tail vein injection (HDVI) models of hepatocellular carcinoma (HCC), different subtypes of HCC derived from different genetic abbreviations can be generated. With the proteome, transcriptome, and genetic abbreviation data of the better-defined HCC from HDVI models and HCC patients, the patient-derived HCC can be classified according to the origin of the genetic abbreviation used in the generation of the HDVI-derived HCC. Furthermore, the proteome of patient-derived plasma will be analyzed, and the signature change unique to HCC classified by different origins of genetic abbreviation will provide an abstract of the proteome, transcriptome, and genetic abbreviation of the HCC tissue. The transcriptomics and proteomics data of HDVI mice produced from five distinct sets of genetic abbreviations have been measured. The proteome and transcriptome of tissue, as well as the proteome of plasma, from a small cohort of HCC patients were studied and classified using this resource to illuminate the fundamental landscape of HCC. The final results will be presented and discussed at the meeting. Citation Format: Hoi Tang MA, Chun Yin YU, Lau Yan NG. The central dogma of hepatocellular carcinoma: Genomic, transcriptomic, and proteomic changes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7102.
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Gorbunova, Vera. "COMPARATIVE TRANSCRIPTOMIC OF LONGEVITY." Innovation in Aging 7, Supplement_1 (December 1, 2023): 432. http://dx.doi.org/10.1093/geroni/igad104.1423.
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Abstract Transcriptome analysis provides a nuanced view into the changes that occur in cells and tissues. Transcriptome changes rapidly and reproducibly in response to physiological influences and environmental insults. Recent years have seen an exponential increase in transcriptome data at bulk, single cell and spatial resolution that allows insights into the mechanisms and regulatory pathways of aging and longevity. In this session Drs. Gorbunova (University of Rochester) and Gladyshev (Harvard Medical School) will discuss comparative transcriptomics of longevity across species with diverse lifespans that revealed unique signatures of longevity and the integration of transcriptome and proteome data. Dr. Gladyshev will discuss development of transcriptomic clocks of measuring biological aging. Dr. Artyomov will discuss single-cell resolution approaches to reveal aspects of immune aging in humans, and Dr. Palovics will present the use of transcriptomics to understand rejuvenating effects of heterochronic parabiosis.
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Nesterenko, Maksim, and Aleksei Miroliubov. "From head to rootlet: comparative transcriptomic analysis of a rhizocephalan barnacle Peltogaster reticulata (Crustacea: Rhizocephala)." F1000Research 11 (May 27, 2022): 583. http://dx.doi.org/10.12688/f1000research.110492.1.
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Background: Rhizocephalan barnacles stand out in the diverse world of metazoan parasites. The body of a rhizocephalan female is modified beyond revealing any recognizable morphological features, consisting of the interna, the system of rootlets, and the externa, a sac-like reproductive body. Moreover, rhizocephalans have an outstanding ability to control their hosts, literally turning them into “zombies”. Despite all these amazing traits, there is no genomic and transcriptomic data about any Rhizocephala. Methods: We collected transcriptomes from four body parts of an adult female rhizocephalan Peltogaster reticulata: externa and main, growing, and thoracic parts of the interna. We used all prepared data for the de novo assembly of the reference transcriptome. Next, a set of encoded proteins was determined, the expression levels of protein-coding genes in different parts of the parasite body were calculated and lists of enriched bioprocesses were identified. We also in silico identified and analyzed sets of potential excretory / secretory proteins. Finally, we applied phylostratigraphy and evolutionary transcriptomics approaches to our data. Results: The assembled reference transcriptome included transcripts of 12,620 protein-coding genes and was the first for both P. reticulata and Rhizocephala. Based on the results obtained, the spatial heterogeneity of protein-coding genes expression in different regions of P. reticulata adult female body was established. The results of both transcriptomic analysis and histological studies indicated the presence of germ-like cells in the lumen of the interna. The potential molecular basis of the interaction between the nervous system of the host and the parasite's interna was also determined. Given the prolonged expression of development-associated genes, we suggest that rhizocephalans “got stuck in the metamorphosis”, even in their reproductive stage. Conclusions: The results of the first comparative transcriptomic analysis for Rhizocephala not only clarified but also expanded the existing ideas about the biology of this amazing parasites.
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Nesterenko, Maksim, and Aleksei Miroliubov. "From head to rootlet: comparative transcriptomic analysis of a rhizocephalan barnacle Peltogaster reticulata (Crustacea: Rhizocephala)." F1000Research 11 (January 9, 2023): 583. http://dx.doi.org/10.12688/f1000research.110492.2.
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Background: Rhizocephalan barnacles stand out in the diverse world of metazoan parasites. The body of a rhizocephalan female is modified beyond revealing any recognizable morphological features, consisting of the interna, a system of rootlets, and the externa, a sac-like reproductive body. Moreover, rhizocephalans have an outstanding ability to control their hosts, literally turning them into “zombies”. Despite all these amazing traits, there are no genomic or transcriptomic data about any Rhizocephala. Methods: We collected transcriptomes from four body parts of an adult female rhizocephalan Peltogaster reticulata: the externa, and the main, growing, and thoracic parts of the interna. We used all prepared data for the de novo assembly of the reference transcriptome. Next, a set of encoded proteins was determined, the expression levels of protein-coding genes in different parts of the parasite’s body were calculated and lists of enriched bioprocesses were identified. We also in silico identified and analyzed sets of potential excretory / secretory proteins. Finally, we applied phylostratigraphy and evolutionary transcriptomics approaches to our data. Results: The assembled reference transcriptome included transcripts of 12,620 protein-coding genes and was the first for any rhizocephalan. Based on the results obtained, the spatial heterogeneity of protein-coding gene expression in different regions of the adult female body of P. reticulata was established. The results of both transcriptomic analysis and histological studies indicated the presence of germ-like cells in the lumen of the interna. The potential molecular basis of the interaction between the nervous system of the host and the parasite's interna was also determined. Given the prolonged expression of development-associated genes, we suggest that rhizocephalans “got stuck in their metamorphosis”, even at the reproductive stage. Conclusions: The results of the first comparative transcriptomic analysis for Rhizocephala not only clarified but also expanded the existing ideas about the biology of these extraordinary parasites.
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Li, Youcheng, Leann Lac, Qian Liu, and Pingzhao Hu. "ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning." PLOS Computational Biology 20, no. 6 (June 27, 2024): e1012254. http://dx.doi.org/10.1371/journal.pcbi.1012254.
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Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoidance of unpredictable tissue disentanglement steps. Although high-quality cell segmentation algorithms can aid in the extraction of valuable data, traditional methods are frequently non-spatial, do not account for spatial information efficiently, and perform poorly when confronted with the problem of spatial transcriptome cell segmentation with varying shapes. In this study, we propose ST-CellSeg, an image-based machine learning method for spatial transcriptomics that uses manifold for cell segmentation and is novel in its consideration of multi-scale information. We first construct a fully connected graph which acts as a spatial transcriptomic manifold. Using multi-scale data, we then determine the low-dimensional spatial probability distribution representation for cell segmentation. Using the adjusted Rand index (ARI), normalized mutual information (NMI), and Silhouette coefficient (SC) as model performance measures, the proposed algorithm significantly outperforms baseline models in selected datasets and is efficient in computational complexity.
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Tao, Feng, Chuanzhu Fan, Yimin Liu, Subashini Sivakumar, Kurt P. Kowalski, and Edward M. Golenberg. "Optimization and application of non-native Phragmites australis transcriptome assemblies." PLOS ONE 18, no. 1 (January 23, 2023): e0280354. http://dx.doi.org/10.1371/journal.pone.0280354.
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Phragmites australis (common reed) has a cosmopolitan distribution and has been suggested as a model organism for the study of invasive plant species. In North America, the non-native subspecies (ssp. australis) is widely distributed across the contiguous 48 states in the United States and large parts of Canada. Even though millions of dollars are spent annually on Phragmites management, insufficient knowledge of P. australis impeded the efficiency of management. To solve this problem, transcriptomic information generated from multiple types of tissue could be a valuable resource for future studies. Here, we constructed forty-nine P. australis transcriptomes assemblies via different assembly tools and multiple parameter settings. The optimal transcriptome assembly for functional annotation and downstream analyses was selected among these transcriptome assemblies by comprehensive assessments. For a total of 422,589 transcripts assembled in this transcriptome assembly, 319,046 transcripts (75.5%) have at least one functional annotation. Within the transcriptome assembly, we further identified 1,495 transcripts showing tissue-specific expression pattern, 10,828 putative transcription factors, and 72,165 candidates for simple sequence repeats markers. The identification and analyses of predicted transcripts related to herbicide- and salinity-resistant genes were shown as two applications of the transcriptomic information to facilitate further research on P. australis. Transcriptome assembly and selection would be important for the transcriptome annotation. With this optimal transcriptome assembly and all relative information from downstream analyses, we have helped to establish foundations for future studies on the mechanisms underlying the invasiveness of non-native P. australis subspecies.
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Lv, Zhuo, Shuaijun Jiang, Shuxin Kong, Xu Zhang, Jiahui Yue, Wanqi Zhao, Long Li, and Shuyan Lin. "Advances in Single-Cell Transcriptome Sequencing and Spatial Transcriptome Sequencing in Plants." Plants 13, no. 12 (June 18, 2024): 1679. http://dx.doi.org/10.3390/plants13121679.
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“Omics” typically involves exploration of the structure and function of the entire composition of a biological system at a specific level using high-throughput analytical methods to probe and analyze large amounts of data, including genomics, transcriptomics, proteomics, and metabolomics, among other types. Genomics characterizes and quantifies all genes of an organism collectively, studying their interrelationships and their impacts on the organism. However, conventional transcriptomic sequencing techniques target population cells, and their results only reflect the average expression levels of genes in population cells, as they are unable to reveal the gene expression heterogeneity and spatial heterogeneity among individual cells, thus masking the expression specificity between different cells. Single-cell transcriptomic sequencing and spatial transcriptomic sequencing techniques analyze the transcriptome of individual cells in plant or animal tissues, enabling the understanding of each cell’s metabolites and expressed genes. Consequently, statistical analysis of the corresponding tissues can be performed, with the purpose of achieving cell classification, evolutionary growth, and physiological and pathological analyses. This article provides an overview of the research progress in plant single-cell and spatial transcriptomics, as well as their applications and challenges in plants. Furthermore, prospects for the development of single-cell and spatial transcriptomics are proposed.
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Alsalloum, Saleh Ali, Lujain Yousef Almulhim, Muna Ali Almakhaita, Atheer Alsubiee, Jawaher Ibrahim Almulhim, Ola Abdullah Aljaafari, Jawaher Alhussain, et al. "Exploring the frontiers of transcriptomics: Methods, applications, and future perspectives." International journal of health sciences 8, S1 (January 27, 2024): 1713–33. http://dx.doi.org/10.53730/ijhs.v8ns1.15376.
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Background: By allowing for the thorough characterization of gene expression, transcriptomics—the study of RNA transcripts generated by the genome—has transformed molecular biology and biomedical research. This area of study offers vital insights into the functioning dynamics of biological systems, cellular mechanisms, and the course of disease. The speed of transcriptomics research has increased due to developments in high-throughput sequencing technologies, especially RNA sequencing (RNA-seq), which enables researchers to examine bulk and single-cell transcriptomes with previously unheard-of resolution. Our comprehension of transcriptome data in the larger framework of omics sciences is further improved by the incorporation of bioinformatics techniques. Aim: this study is to give a thorough introduction to transcriptomics, emphasizing its methods, uses, and difficulties. It also aims to draw attention to current developments in the subject and how they affect environmental sciences, health, and medicine. Methods: Results from peer-reviewed publications published between 2020 and 2024 are combined in this study. Together with bioinformatics tools for data processing, it critically evaluates transcriptome techniques such as RNA-seq, single-cell transcriptomics, and spatial transcriptomics. With an emphasis on integrated omics, applications in ecological studies, biotechnology, and disease research are examined. Findings: Transcriptomics has greatly improved our comprehension of intricate biological processes.
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Liu, Bin, Bodo Rosenhahn, Thomas Illig, and David S. DeLuca. "A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data." PLOS Computational Biology 20, no. 7 (July 3, 2024): e1011198. http://dx.doi.org/10.1371/journal.pcbi.1011198.
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Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize deep learning techniques, specifically autoencoder architectures, to learn latent variables that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are capable of learning reduced representations of transcriptomes that retain critical biological information. We propose a novel VAE that utilizes priors from biological data to direct the network to learn a representation of the transcriptome that is based on understandable biological concepts. After benchmarking five different autoencoder architectures, we found that each succeeded in reducing the transcriptomes to 50 latent dimensions, which captured enough variation for accurate reconstruction. The simple, fully connected autoencoder, performs best across the benchmarks, but lacks the characteristic of having directly interpretable latent dimensions. The beta-weighted, prior-informed VAE implementation is able to solve the benchmarking tasks, and provide semantically accurate latent features equating to biological pathways. This study opens a new direction for differential pathway analysis in transcriptomics with increased transparency and interpretability.
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Macrander, Jason, Jyothirmayi Panda, Daniel Janies, Marymegan Daly, and Adam M. Reitzel. "Venomix: a simple bioinformatic pipeline for identifying and characterizing toxin gene candidates from transcriptomic data." PeerJ 6 (July 31, 2018): e5361. http://dx.doi.org/10.7717/peerj.5361.
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The advent of next-generation sequencing has resulted in transcriptome-based approaches to investigate functionally significant biological components in a variety of non-model organism. This has resulted in the area of “venomics”: a rapidly growing field using combined transcriptomic and proteomic datasets to characterize toxin diversity in a variety of venomous taxa. Ultimately, the transcriptomic portion of these analyses follows very similar pathways after transcriptome assembly often including candidate toxin identification using BLAST, expression level screening, protein sequence alignment, gene tree reconstruction, and characterization of potential toxin function. Here we describe the Python package Venomix, which streamlines these processes using common bioinformatic tools along with ToxProt, a publicly available annotated database comprised of characterized venom proteins. In this study, we use the Venomix pipeline to characterize candidate venom diversity in four phylogenetically distinct organisms, a cone snail (Conidae; Conus sponsalis), a snake (Viperidae; Echis coloratus), an ant (Formicidae; Tetramorium bicarinatum), and a scorpion (Scorpionidae; Urodacus yaschenkoi). Data on these organisms were sampled from public databases, with each original analysis using different approaches for transcriptome assembly, toxin identification, or gene expression quantification. Venomix recovered numerically more candidate toxin transcripts for three of the four transcriptomes than the original analyses and identified new toxin candidates. In summary, we show that the Venomix package is a useful tool to identify and characterize the diversity of toxin-like transcripts derived from transcriptomic datasets. Venomix is available at: https://bitbucket.org/JasonMacrander/Venomix/.
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Ungar, B., M. Yavzori, E. Fudim, O. Picard, U. Kopylov, R. Eliakim, D. Shouval, et al. "P032 Host transcriptome signatures in human fecal-washes predict histological remission in IBD patients." Journal of Crohn's and Colitis 16, Supplement_1 (January 1, 2022): i152—i153. http://dx.doi.org/10.1093/ecco-jcc/jjab232.161.
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Abstract Background Colonoscopy is the gold standard for evaluation of inflammation in inflammatory bowel diseases (IBD), yet entails cumbersome preparations and risks of injury. Existing non-invasive prognostic tools are limited in their diagnostic power. Moreover, transcriptomics of colonic biopsies have been inconclusive in their association with clinical features. Our aim was to assess the utility of host transcriptomics of fecal wash samples of IBD patients compared to controls. Methods In this prospective cohort study, we obtained biopsies and fecal-wash samples from IBD patients and controls undergoing lower endoscopy. We performed RNAseq of biopsies and matching fecal-washes, and associated them with endoscopic and histological inflammation status. We also performed fecal mass-spectrometry proteomics on a subset of samples. We inferred cell compositions using computational deconvolution and used classification algorithms to identify informative genes. Results We analyzed biopsies and fecal washes from 39 patients (19 IBD, 20 controls). Host fecal-transcriptome carried information that was distinct from biopsy RNAseq and fecal proteomics. Transcriptomics of fecal washes, yet not of biopsies, from patients with histological inflammation were significantly correlated to one another (p=5.3*10–12). Fecal-transcriptome was significantly more powerful in identifying histological inflammation compared to transcriptome of intestinal biopsies (150 genes with area-under-the-curve >0.9 in fecal samples versus 10 genes in biopsy RNAseq). Fecal samples were enriched in inflammatory monocytes, regulatory T cells, natural killer-cells and innate lymphoid cells. Figure 1 - Fecal-wash host transcriptome predicts histological inflammation. A) Study layout, B) Clustergram of fecal-wash host cell mRNA signatures, demonstrating that patients with histological inflammation (red) are clustered when measuring fecal wash transcriptome yet not biopsy transcriptomes. C-D) Principle Component Analysis demonstrating improved separation of inflamed patients based on fecal host transcriptomes. E, F) Expression of host genes in fecal washes has higher statistical power (Area under the Curve, AUC) in classifying histological inflammation compared to biopsies. D shows NFKBIA as an example, E shows the AUC of the 5% best classifying genes, F shows the overall AUC based on biopsies or washes. Gray areas have AUC>0.9. G) UMAP of cells obtained from scRNAseq of mouse small intestine fecal washes. Conclusion Fecal wash host transcriptome is a powerful biomarker reflecting histological inflammation. Furthermore, it opens the way to identifying important correlates and therapeutic targets that may be obscure using biopsy transctriptomics.
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Ortiz, Randy, Priyanka Gera, Christopher Rivera, and Juan C. Santos. "Pincho: A Modular Approach to High Quality De Novo Transcriptomics." Genes 12, no. 7 (June 22, 2021): 953. http://dx.doi.org/10.3390/genes12070953.
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Transcriptomic reconstructions without reference (i.e., de novo) are common for data samples derived from non-model biological systems. These assemblies involve massive parallel short read sequence reconstructions from experiments, but they usually employ ad-hoc bioinformatic workflows that exhibit limited standardization and customization. The increasing number of transcriptome assembly software continues to provide little room for standardization which is exacerbated by the lack of studies on modularity that compare the effects of assembler synergy. We developed a customizable management workflow for de novo transcriptomics that includes modular units for short read cleaning, assembly, validation, annotation, and expression analysis by connecting twenty-five individual bioinformatic tools. With our software tool, we were able to compare the assessment scores based on 129 distinct single-, bi- and tri-assembler combinations with diverse k-mer size selections. Our results demonstrate a drastic increase in the quality of transcriptome assemblies with bi- and tri- assembler combinations. We aim for our software to improve de novo transcriptome reconstructions for the ever-growing landscape of RNA-seq data derived from non-model systems. We offer guidance to ensure the most complete transcriptomic reconstructions via the inclusion of modular multi-assembly software controlled from a single master console.
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Zhou, Jun, Shengxi Wang, Ming Liu, and Zhaopei Li. "Effect of cryoablation on the spatial transcriptomic landscape of the immune microenvironment in non-small cell lung cancer." Journal of Cancer Research and Therapeutics 20, no. 7 (December 2024): 2141–47. https://doi.org/10.4103/jcrt.jcrt_1887_24.
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ABSTRACT Background: Cryoablation induces antitumor immune responses. Spatial transcriptomic landscape technology has been used to determine the micron-level panoramic transcriptomics of tissue slices in situ. Methods: The effects of cryoablation on the immune microenvironment in non-small cell lung cancer (NSCLC) were explored by comparing the Whole Transcriptome Atlas (WTA) panel of immune cells before and after cryoablation using the spatial transcriptomic landscape. Results: The bioinformatics analysis showed that cryoablation significantly affected the WTA of immune cells, particularly genes related to cellular components, biological processes, molecular functions, proliferation and migration, and cytokine-cytokine receptor interaction signaling pathways. Conclusions: The findings of this study suggest that cryoablation significantly impacts the biological functions of immune cells in the tumor microenvironment of NSCLC through multiple mechanisms.
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Packer, Jonathan S., Qin Zhu, Chau Huynh, Priya Sivaramakrishnan, Elicia Preston, Hannah Dueck, Derek Stefanik, et al. "A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution." Science 365, no. 6459 (September 5, 2019): eaax1971. http://dx.doi.org/10.1126/science.aax1971.
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Caenorhabditis elegans is an animal with few cells but a wide diversity of cell types. In this study, we characterize the molecular basis for their specification by profiling the transcriptomes of 86,024 single embryonic cells. We identify 502 terminal and preterminal cell types, mapping most single-cell transcriptomes to their exact position in C. elegans’ invariant lineage. Using these annotations, we find that (i) the correlation between a cell’s lineage and its transcriptome increases from middle to late gastrulation, then falls substantially as cells in the nervous system and pharynx adopt their terminal fates; (ii) multilineage priming contributes to the differentiation of sister cells at dozens of lineage branches; and (iii) most distinct lineages that produce the same anatomical cell type converge to a homogenous transcriptomic state.
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Kordonowy, Lauren L., and Matthew D. MacManes. "Characterization of a male reproductive transcriptome forPeromyscus eremicus(Cactus mouse)." PeerJ 4 (October 27, 2016): e2617. http://dx.doi.org/10.7717/peerj.2617.
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Rodents of the genusPeromyscushave become increasingly utilized models for investigations into adaptive biology. This genus is particularly powerful for research linking genetics with adaptive physiology or behaviors, and recent research has capitalized on the unique opportunities afforded by the ecological diversity of these rodents. Well characterized genomic and transcriptomic data is intrinsic to explorations of the genetic architecture responsible for ecological adaptations. Therefore, this study characterizes the transcriptome of three male reproductive tissues (testes, epididymis and vas deferens) ofPeromyscus eremicus(Cactus mouse), a desert specialist. The transcriptome assembly process was optimized in order to produce a high quality and substantially complete annotated transcriptome. This composite transcriptome was generated to characterize the expressed transcripts in the male reproductive tract ofP. eremicus,which will serve as a crucial resource for future research investigating our hypothesis that the male Cactus mouse possesses an adaptive reproductive phenotype to mitigate water-loss from ejaculate. This study reports genes under positive selection in the male Cactus mouse reproductive transcriptome relative to transcriptomes fromPeromyscus maniculatus(deer mouse) andMus musculus.Thus, this study expands upon existing genetic research in this species, and we provide a high quality transcriptome to enable further explorations of our proposed hypothesis for male Cactus mouse reproductive adaptations to minimize seminal fluid loss.
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Ronza, Paolo, José Antonio Álvarez-Dios, Diego Robledo, Ana Paula Losada, Roberto Romero, Roberto Bermúdez, Belén G. Pardo, Paulino Martínez, and María Isabel Quiroga. "Blood Transcriptomics of Turbot Scophthalmus maximus: A Tool for Health Monitoring and Disease Studies." Animals 11, no. 5 (April 30, 2021): 1296. http://dx.doi.org/10.3390/ani11051296.
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Blood transcriptomics is emerging as a relevant tool to monitor the status of the immune system and assist in diagnosis, prognosis, treatment and pathogenesis studies of diseases. In fish pathology, the potential of transcriptome profiling of blood is still poorly explored. Here, RNA sequencing was applied to analyze the blood transcriptional profile of turbot (Scophthalmus maximus), the most important farmed flatfish. The study was conducted in healthy specimens and specimens parasitized by the myxozoan Enteromyxum scophthalmi, which causes one of the most devastating diseases in turbot aquaculture. The blood of healthy turbot showed a transcriptomic profile mainly related to erythrocyte gas transportation function, but also to antigen processing and presentation. In moderately infected turbot, the blood reflected a broad inhibition of the immune response. Particularly, down-regulation of the B cell receptor signaling pathway was shared with heavily parasitized fish, which showed larger transcriptomic changes, including the activation of the inflammatory response. Turbot response to enteromyxosis proved to be delayed, dysregulated and ineffective in stopping the infection. The study evinces that blood transcriptomics can contribute to a better understanding of the teleost immune system and serve as a reliable tool to investigate the physiopathological status of fish.
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Smith, William J., David L. Cedeño, Samuel M. Thomas, Courtney A. Kelley, Francesco Vetri, and Ricardo Vallejo. "Modulation of microglial activation states by spinal cord stimulation in an animal model of neuropathic pain: Comparing high rate, low rate, and differential target multiplexed programming." Molecular Pain 17 (January 2021): 174480692199901. http://dx.doi.org/10.1177/1744806921999013.
Full textAbstract:
While numerous studies and patient experiences have demonstrated the efficacy of spinal cord stimulation as a treatment for chronic neuropathic pain, the exact mechanism underlying this therapy is still uncertain. Recent studies highlighting the importance of microglial cells in chronic pain and characterizing microglial activation transcriptomes have created a focus on microglia in pain research. Our group has investigated the modulation of gene expression in neurons and glial cells after spinal cord stimulation (SCS), specifically focusing on transcriptomic changes induced by varying SCS stimulation parameters. Previous work showed that, in rodents subjected to the spared nerve injury (SNI) model of neuropathic pain, a differential target multiplexed programming (DTMP) approach provided significantly better relief of pain-like behavior compared to high rate (HRP) and low rate programming (LRP). While these studies demonstrated the importance of transcriptomic changes in SCS mechanism of action, they did not specifically address the role of SCS in microglial activation. The data presented herein utilizes microglia-specific activation transcriptomes to further understand how an SNI model of chronic pain and subsequent continuous SCS treatment with either DTMP, HRP, or LRP affects microglial activation. Genes for each activation transcriptome were identified within our dataset and gene expression levels were compared with that of healthy animals, naïve to injury and interventional procedures. Pearson correlations indicated that DTMP yields the highest significant correlations to expression levels found in the healthy animals across all microglial activation transcriptomes. In contrast, HRP or LRP yielded weak or very weak correlations for these transcriptomes. This work demonstrates that chronic pain and subsequent SCS treatments can modulate microglial activation transcriptomes, supporting previous research on microglia in chronic pain. Furthermore, this study provides evidence that DTMP is more effective than HRP and LRP at modulating microglial transcriptomes, offering potential insight into the therapeutic efficacy of DTMP.
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Gui, Yu, Xiujing He, Jing Yu, and Jing Jing. "Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy." Journal of Clinical Medicine 12, no. 4 (February 6, 2023): 1279. http://dx.doi.org/10.3390/jcm12041279.
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The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.
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Jiang, Peng. "Abstract IA002: Inference of intercellular signaling activities in tumor spatial and single-cell transcriptomics, with applications in identifying cancer immunotherapy targets." Molecular Cancer Therapeutics 22, no. 12_Supplement (December 1, 2023): IA002. http://dx.doi.org/10.1158/1535-7163.targ-23-ia002.
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Abstract My talk will present three computational frameworks we developed to study cytokine signaling activities and cell-cell communications during the antitumor immune response, using tumor single-cell and spatial transcriptomics. The basic immunology tool to study cytokine signaling mostly measures cytokine release, which is transient and does not represent downstream target activities. Therefore, we first developed the CytoSig platform, providing a database of target genes modulated by cytokines and a predictive model of cytokine signaling cascades from transcriptomic profiles. We collected 20,591 transcriptome profiles for human cytokine, chemokine, and growth factor responses. This atlas of transcriptional patterns induced by cytokines enabled the reliable prediction of signaling activities in distinct cell populations in infectious diseases, chronic inflammation, and cancer using bulk and single-cell transcriptomic data. CytoSig revealed previously unidentified roles of many cytokines, such as BMP6 as an anti-inflammatory factor. Then, based on CytoSig, we developed Tres, a computational model utilizing single-cell transcriptomic data to identify signatures of T cells that are resilient to immunosuppressive signals, such as TGF-β1, TRAIL, and prostaglandin E2. Tres reliably predicts clinical responses to immunotherapy in multiple cancer types using bulk T cell transcriptomic data from pre-treatment patient tumors or infusion/pre-manufacture samples for cellular immunotherapies. Further, Tres identified FIBP as a candidate immunotherapy target to potentiate adoptive cell therapy efficacies. FIBP knockout in T cells enhanced adoptive cell therapy by down-regulating T cells' cholesterol metabolism. Last, I will briefly show our SpaCET framework for deconvolving cell fractions and identifying cell-cell interactions in tumor spatial transcriptomics data. SpaCET resolved several challenges in spatial transcriptomics analysis that previous methods did not address sufficiently. Through coupling cell fractions with ligand-receptor co-expression analysis, SpaCET reveals that intercellular interactions tend to be located at the tumor-immune boundaries. Citation Format: Peng Jiang. Inference of intercellular signaling activities in tumor spatial and single-cell transcriptomics, with applications in identifying cancer immunotherapy targets [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr IA002.
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Krüger, Manuela, Oushadee A. J. Abeyawardana, Claudia Krüger, Miloslav Juříček, and Helena Štorchová. "Differentially Expressed Genes Shared by Two Distinct Cytoplasmic Male Sterility (CMS) Types of Silene vulgaris Suggest the Importance of Oxidative Stress in Pollen Abortion." Cells 9, no. 12 (December 16, 2020): 2700. http://dx.doi.org/10.3390/cells9122700.
Full textAbstract:
Cytoplasmic male sterility (CMS), encoded by the interacting mitochondrial and nuclear genes, causes pollen abortion or non-viability. CMS is widely used in agriculture and extensively studied in crops. Much less is known about CMS in wild species. We performed a comparative transcriptomic analysis of male sterile and fertile individuals of Silene vulgaris, a model plant for the study of gynodioecy, to reveal the genes responsible for pollen abortion in this species. We used RNA-seq datasets previously employed for the analysis of mitochondrial and plastid transcriptomes of female and hermaphrodite flower buds, making it possible to compare the transcriptomes derived from three genomes in the same RNA specimen. We assembled de novo transcriptomes for two haplotypes of S. vulgaris and identified differentially expressed genes between the females and hermaphrodites, associated with stress response or pollen development. The gene for alternative oxidase was downregulated in females. The genetic pathways controlling CMS in S. vulgaris are similar to those in crops. The high number of the differentially expressed nuclear genes contrasts with the uniformity of organellar transcriptomes across genders, which suggests these pathways are evolutionarily conserved and that selective mechanisms may shield organellar transcription against changes in the cytoplasmic transcriptome.
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Londin, Eric R., Eleftheria Hatzimichael, Phillipe Loher, Leonard C. Edelstein, Chad Shaw, Kathleen Delgrosso, Paolo M. Fortina, Paul F. Bray, Steven E. McKenzie, and Isidore Rigoutsos. "Towards a Reference Human Platelet Transcriptome: Evaluation Of Inter-Individual Correlations and Its Relationship With a Platelet Proteome." Blood 122, no. 21 (November 15, 2013): 2297. http://dx.doi.org/10.1182/blood.v122.21.2297.2297.
Full textAbstract:
Abstract Next generation sequencing of RNA (RNA-seq) is an emerging technology that has so far been used successfully to profile the transcriptomes of several cell types and cell states. For the platelet transcriptome, RNA-seq descriptions exist for only a few subjects. Additionally, there have been no studies of the same individual’s transcriptome using two different technologies. As such, it has been unclear how well platelet transcriptomes correlate among different donors or across different RNA platforms, and what the transcriptomes’ relationship is with the platelet proteome. We generated RNA-seq profiles of the long RNA transcriptomes from the platelets of 10 healthy young males (5 white and 5 black). In addition to RNA-seq, we profiled the platelet messenger RNAs of the same 10 individuals using the Affymetrix GeneChip System. We observed that the abundance of platelet mRNA transcripts was highly correlated across the 10 individuals, a finding that was independent of race and of the employed technology. Additionally, our RNA-seq data showed that these high inter-individual correlations extend beyond mRNAs to several categories of non-coding RNAs. However, there was a notable exception: the category of pseudogenes exhibited a clear difference in expression by race. Comparison of our mRNA signatures with the only publicly available quantitative platelet proteome data showed that most (87.5%) identified platelet proteins had a detectable corresponding mRNA. Interestingly, there was also a high number of mRNAs that were present in the transcriptomes of all 10 individuals but had no representation in the proteome. Spearman correlation of the relative abundances for those platelet genes that were represented by both an mRNA and a protein, revealed an unexpectedly weak correlation between the transcriptome and the proteome. Further analysis of the overlapping and non-overlapping platelet mRNAs and proteins identified groups of genes with very distinct characteristics. Gene Ontology analysis of the respective gene identifiers revealed that the gene groups corresponded to distinct cellular processes, an interesting finding that provides novel insights for platelet biology. The very high inter-individual correlations of the transcriptome signatures across 10 different subjects representing two races together with the results of our analyses indicate that it is feasible to assemble a platelet mRNA-ome that can serve as a reference for future platelet transcriptomic studies of human health and disease. Disclosures: No relevant conflicts of interest to declare.
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Darden, Dijoia B., Gabriela L. Ghita, Zhongkai Wang, Julie A. Stortz, Maria-Cecilia Lopez, Michael C. Cox, Russell B. Hawkins, et al. "Chronic Critical Illness Elicits a Unique Circulating Leukocyte Transcriptome in Sepsis Survivors." Journal of Clinical Medicine 10, no. 15 (July 21, 2021): 3211. http://dx.doi.org/10.3390/jcm10153211.
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Surgical sepsis has evolved into two major subpopulations: patients who rapidly recover, and those who develop chronic critical illness (CCI). Our primary aim was to determine whether CCI sepsis survivors manifest unique blood leukocyte transcriptomes in late sepsis that differ from transcriptomes among sepsis survivors with rapid recovery. In a prospective cohort study of surgical ICU patients, genome-wide expression analysis was conducted on total leukocytes in human whole blood collected on days 1 and 14 from sepsis survivors who rapidly recovered or developed CCI, defined as ICU length of stay ≥ 14 days with persistent organ dysfunction. Both sepsis patients who developed CCI and those who rapidly recovered exhibited marked changes in genome-wide expression at day 1 which remained abnormal through day 14. Although summary changes in gene expression were similar between CCI patients and subjects who rapidly recovered, CCI patients exhibited differential expression of 185 unique genes compared with rapid recovery patients at day 14 (p < 0.001). The transcriptomic patterns in sepsis survivors reveal an ongoing immune dyscrasia at the level of the blood leukocyte transcriptome, consistent with persistent inflammation and immune suppression. Furthermore, the findings highlight important genes that could compose a prognostic transcriptomic metric or serve as therapeutic targets among sepsis patients that develop CCI.
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Cheon, Seongmin, Sung-Gwon Lee, Hyun-Hee Hong, Hyun-Gwan Lee, Kwang Young Kim, and Chungoo Park. "A guide to phylotranscriptomic analysis for phycologists." Algae 36, no. 4 (December 15, 2021): 333–40. http://dx.doi.org/10.4490/algae.2021.36.12.7.
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Phylotranscriptomics is the study of phylogenetic relationships among taxa based on their DNA sequences derived from transcriptomes. Because of the relatively low cost of transcriptome sequencing compared with genome sequencing and the fact that phylotranscriptomics is almost as reliable as phylogenomics, the phylotranscriptomic analysis has recently emerged as the preferred method for studying evolutionary biology. However, it is challenging to perform transcriptomic and phylogenetic analyses together without programming expertise. This study presents a protocol for phylotranscriptomic analysis to aid marine biologists unfamiliar with UNIX command-line interface and bioinformatics tools. Here, we used transcriptomes to reconstruct a molecular phylogeny of dinoflagellate protists, a diverse and globally abundant group of marine plankton organisms whose large and complex genomic sequences have impeded conventional phylogenic analysis based on genomic data. We hope that our proposed protocol may serve as practical and helpful information for the training and education of novice phycologists.
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Herrera-Uribe, Juber, Kristen A. Byrne, Haibo Liu, Sage Becker, Crystal L. Loving, and Christopher K. Tuggle. "The transcriptional landscape of porcine peripheral blood immune cells." Journal of Immunology 204, no. 1_Supplement (May 1, 2020): 92.18. http://dx.doi.org/10.4049/jimmunol.204.supp.92.18.
Full textAbstract:
Abstract Blood consists of different immune cells with essential functions given by distinct molecular signatures including transcriptomic profiles. The porcine immune system shares many similarities with that in human; however, the porcine immune cell transcriptome has not been as comprehensively studied. To identify distinct transcriptomic profiles of major porcine peripheral blood immune cells, we employed magnetic separation and fluorescence activated cell sorting methods to isolate 9 different cell types from blood PBMCs from two healthy pigs, representing monocytes, neutrophils, NK cells and specific populations of T and B cells. We obtained deep individual cell type transcriptomes using RNA-seq, detecting up to 10,974 genes. Principal component analysis revealed appropriate clustering of replicate samples and the separation of T, B, NK and myeloid cell groups. Pairwise comparisons revealed significant expression of a large number of genes specific for monocytes, neutrophils and NK cells only, while enrichment analysis identified highly differentially expressed genes for all cell types. Gene Ontology term analysis showed high enrichment of biological processes related to the nature of each cell type (i.e., B cell antigen presentation enriched in B cell transcriptome). The gene enrichment and specific genes of porcine immune cells is being further validated in silico to human datasets using gene set enrichment analysis (GSEA), and in vitro using qRT-PCR analysis. Taken together, the gene expression profiles reported here is the first comprehensive transcriptomic study of circulating porcine immune cell types and provides a valuable resource to elucidate molecular markers for porcine immune cell identity and function.
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Salazar, Juan Alfonso, Cristian Vergara-Pulgar, Claudia Jorquera, Patricio Zapata, David Ruiz, Pedro Martínez-Gómez, Rodrigo Infante, and Claudio Meneses. "De Novo Transcriptome Sequencing in Kiwifruit (Actinidia chinensis var. deliciosa (A Chev) Liang et Ferguson) and Development of Tissue-Specific Transcriptomic Resources." Agronomy 11, no. 5 (May 7, 2021): 919. http://dx.doi.org/10.3390/agronomy11050919.
Full textAbstract:
Kiwifruit (Actinidia chinensis var. deliciosa (A Chev) Liang et Ferguson) is a sub-tropical vine species from the Actinidiaceae family native to China. This species has an allohexaploid genome (from diploid and autotetraploid parents), contained in 174 chromosomes producing a climacteric and fleshy fruit called kiwifruit. Currently, only a small body of transcriptomic and proteomic data are available for A. chinensis var. deliciosa. In this low molecular knowledge context, the main goal of this study is to construct a tissue-specific de novo transcriptome assembly, generating differential expression analysis among these specific tissues, to obtain new useful transcriptomic information for a better knowledge of vegetative, floral and fruit growth in this species. In this study, we have analyzed different whole transcriptomes from shoot, leaf, flower bud, flower and fruit at four development stages (7, 50, 120 and 160 days after flowering; DAF) in kiwifruit obtained through RNA-seq sequencing. The first version of the developed A. chinensis var. deliciosa de novo transcriptome contained 142,025 contigs (x¯ = 1044 bp, N50 = 1133 bp). Annotation was performed with BLASTX against the TAIR10 protein database, and we found an annotation proportion of 35.6% (50,508), leaving 64.4% (91,517) of the contigs without annotation. These results represent a reference transcriptome for allohexaploid kiwifruit generating a database of A. chinensis var. deliciosa genes related to leaf, flower and fruit development. These results provided highly valuable information identifying over 20,000 exclusive genes including all tissue comparisons, which were associated with the proteins involved in different biological processes and molecular functions.
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Wang, Changli, Lijun Chen, Yaobin Chen, Wenwen Jia, Xunhui Cai, Yufeng Liu, Fenghu Ji, et al. "Abnormal global alternative RNA splicing in COVID-19 patients." PLOS Genetics 18, no. 4 (April 14, 2022): e1010137. http://dx.doi.org/10.1371/journal.pgen.1010137.
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Viral infections can alter host transcriptomes by manipulating host splicing machinery. Despite intensive transcriptomic studies on SARS-CoV-2, a systematic analysis of alternative splicing (AS) in severe COVID-19 patients remains largely elusive. Here we integrated proteomic and transcriptomic sequencing data to study AS changes in COVID-19 patients. We discovered that RNA splicing is among the major down-regulated proteomic signatures in COVID-19 patients. The transcriptome analysis showed that SARS-CoV-2 infection induces widespread dysregulation of transcript usage and expression, affecting blood coagulation, neutrophil activation, and cytokine production. Notably, CD74 and LRRFIP1 had increased skipping of an exon in COVID-19 patients that disrupts a functional domain, which correlated with reduced antiviral immunity. Furthermore, the dysregulation of transcripts was strongly correlated with clinical severity of COVID-19, and splice-variants may contribute to unexpected therapeutic activity. In summary, our data highlight that a better understanding of the AS landscape may aid in COVID-19 diagnosis and therapy.
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Duan, Hao, Qingchen Zhang, Feifei Cui, Quan Zou, and Zilong Zhang. "MVST: Identifying spatial domains of spatial transcriptomes from multiple views using multi-view graph convolutional networks." PLOS Computational Biology 20, no. 9 (September 5, 2024): e1012409. http://dx.doi.org/10.1371/journal.pcbi.1012409.
Full textAbstract:
Spatial transcriptome technology can parse transcriptomic data at the spatial level to detect high-throughput gene expression and preserve information regarding the spatial structure of tissues. Identifying spatial domains, that is identifying regions with similarities in gene expression and histology, is the most basic and critical aspect of spatial transcriptome data analysis. Most current methods identify spatial domains only through a single view, which may obscure certain important information and thus fail to make full use of the information embedded in spatial transcriptome data. Therefore, we propose an unsupervised clustering framework based on multiview graph convolutional networks (MVST) to achieve accurate spatial domain recognition by the learning graph embedding features of neighborhood graphs constructed from gene expression information, spatial location information, and histopathological image information through multiview graph convolutional networks. By exploring spatial transcriptomes from multiple views, MVST enables data from all parts of the spatial transcriptome to be comprehensively and fully utilized to obtain more accurate spatial expression patterns. We verified the effectiveness of MVST on real spatial transcriptome datasets, the robustness of MVST on some simulated datasets, and the reasonableness of the framework structure of MVST in ablation experiments, and from the experimental results, it is clear that MVST can achieve a more accurate spatial domain identification compared with the current more advanced methods. In conclusion, MVST is a powerful tool for spatial transcriptome research with improved spatial domain recognition.
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Hofmann, Erich P., Rhett M. Rautsaw, Andrew J. Mason, Jason L. Strickland, and Christopher L. Parkinson. "Duvernoy’s Gland Transcriptomics of the Plains Black-Headed Snake, Tantilla nigriceps (Squamata, Colubridae): Unearthing the Venom of Small Rear-Fanged Snakes." Toxins 13, no. 5 (May 6, 2021): 336. http://dx.doi.org/10.3390/toxins13050336.
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The venoms of small rear-fanged snakes (RFS) remain largely unexplored, despite increased recognition of their importance in understanding venom evolution more broadly. Sequencing the transcriptome of venom-producing glands has greatly increased the ability of researchers to examine and characterize the toxin repertoire of small taxa with low venom yields. Here, we use RNA-seq to characterize the Duvernoy’s gland transcriptome of the Plains Black-headed Snake, Tantilla nigriceps, a small, semi-fossorial colubrid that feeds on a variety of potentially dangerous arthropods including centipedes and spiders. We generated transcriptomes of six individuals from three localities in order to both characterize the toxin expression of this species for the first time, and to look for initial evidence of venom variation in the species. Three toxin families—three-finger neurotoxins (3FTxs), cysteine-rich secretory proteins (CRISPs), and snake venom metalloproteinases (SVMPIIIs)—dominated the transcriptome of T. nigriceps; 3FTx themselves were the dominant toxin family in most individuals, accounting for as much as 86.4% of an individual’s toxin expression. Variation in toxin expression between individuals was also noted, with two specimens exhibiting higher relative expression of c-type lectins than any other sample (8.7–11.9% compared to <1%), and another expressed CRISPs higher than any other toxin. This study provides the first Duvernoy’s gland transcriptomes of any species of Tantilla, and one of the few transcriptomic studies of RFS not predicated on a single individual. This initial characterization demonstrates the need for further study of toxin expression variation in this species, as well as the need for further exploration of small RFS venoms.
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Udaondo, Zulema, Kanchana Sittikankaew, Tanaporn Uengwetwanit, Thidathip Wongsurawat, Chutima Sonthirod, Piroon Jenjaroenpun, Wirulda Pootakham, Nitsara Karoonuthaisiri, and Intawat Nookaew. "Comparative Analysis of PacBio and Oxford Nanopore Sequencing Technologies for Transcriptomic Landscape Identification of Penaeus monodon." Life 11, no. 8 (August 23, 2021): 862. http://dx.doi.org/10.3390/life11080862.
Full textAbstract:
With the advantages that long-read sequencing platforms such as Pacific Biosciences (Menlo Park, CA, USA) (PacBio) and Oxford Nanopore Technologies (Oxford, UK) (ONT) can offer, various research fields such as genomics and transcriptomics can exploit their benefits. Selecting an appropriate sequencing platform is undoubtedly crucial for the success of the research outcome, thus there is a need to compare these long-read sequencing platforms and evaluate them for specific research questions. This study aims to compare the performance of PacBio and ONT platforms for transcriptomic analysis by utilizing transcriptome data from three different tissues (hepatopancreas, intestine, and gonads) of the juvenile black tiger shrimp, Penaeus monodon. We compared three important features: (i) main characteristics of the sequencing libraries and their alignment with the reference genome, (ii) transcript assembly features and isoform identification, and (iii) correlation of the quantification of gene expression levels for both platforms. Our analyses suggest that read-length bias and differences in sequencing throughput are highly influential factors when using long reads in transcriptome studies. These comparisons can provide a guideline when designing a transcriptome study utilizing these two long-read sequencing technologies.
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Cai, Anran, Lützen Portengen, Gökhan Ertaylan, Juliette Legler, Roel Vermeulen, Virissa Lenters, and Sylvie Remy. "Prenatal Exposure to Metabolism-Disrupting Chemicals, Cord Blood Transcriptome Perturbations, and Birth Weight in a Belgian Birth Cohort." International Journal of Molecular Sciences 24, no. 8 (April 20, 2023): 7607. http://dx.doi.org/10.3390/ijms24087607.
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Prenatal exposure to metabolism-disrupting chemicals (MDCs) has been linked to birth weight, but the molecular mechanisms remain largely unknown. In this study, we investigated gene expressions and biological pathways underlying the associations between MDCs and birth weight, using microarray transcriptomics, in a Belgian birth cohort. Whole cord blood measurements of dichlorodiphenyldichloroethylene (p,p’-DDE), polychlorinated biphenyls 153 (PCB-153), perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and transcriptome profiling were conducted in 192 mother–child pairs. A workflow including a transcriptome-wide association study, pathway enrichment analysis with a meet-in-the-middle approach, and mediation analysis was performed to characterize the biological pathways and intermediate gene expressions of the MDC–birth weight relationship. Among 26,170 transcriptomic features, we successfully annotated five overlapping metabolism-related gene expressions associated with both an MDC and birth weight, comprising BCAT2, IVD, SLC25a16, HAS3, and MBOAT2. We found 11 overlapping pathways, and they are mostly related to genetic information processing. We found no evidence of any significant mediating effect. In conclusion, this exploratory study provides insights into transcriptome perturbations that may be involved in MDC-induced altered birth weight.
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Navarrete-López, Paula, Victoria Asselstine, María Maroto, Marta Lombó, Ángela Cánovas, and Alfonso Gutiérrez-Adán. "RNA Sequencing of Sperm from Healthy Cattle and Horses Reveals the Presence of a Large Bacterial Population." Current Issues in Molecular Biology 46, no. 9 (September 19, 2024): 10430–43. http://dx.doi.org/10.3390/cimb46090620.
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RNA molecules within ejaculated sperm can be characterized through whole-transcriptome sequencing, enabling the identification of pivotal transcripts that may influence reproductive success. However, the profiling of sperm transcriptomes through next-generation sequencing has several limitations impairing the identification of functional transcripts. In this study, we explored the nature of the RNA sequences present in the sperm transcriptome of two livestock species, cattle and horses, using RNA sequencing (RNA-seq) technology. Through processing of transcriptomic data derived from bovine and equine sperm cell preparations, low mapping rates to the reference genomes were observed, mainly attributed to the presence of ribosomal RNA and bacteria in sperm samples, which led to a reduced sequencing depth of RNAs of interest. To explore the presence of bacteria, we aligned the unmapped reads to a complete database of bacterial genomes and identified bacteria-associated transcripts which were characterized. This analysis examines the limitations associated with sperm transcriptome profiling by reporting the nature of the RNA sequences among which bacterial RNA was found. These findings can aid researchers in understanding spermatozoal RNA-seq data and pave the way for the identification of molecular markers of sperm performance.
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Chaudhuri, Roy R., Lu Yu, Alpa Kanji, Timothy T. Perkins, Paul P. Gardner, Jyoti Choudhary, Duncan J. Maskell, and Andrew J. Grant. "Quantitative RNA-seq analysis of the Campylobacter jejuni transcriptome." Microbiology 157, no. 10 (October 1, 2011): 2922–32. http://dx.doi.org/10.1099/mic.0.050278-0.
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C ampylobacter jejuni is the most common bacterial cause of foodborne disease in the developed world. Its general physiology and biochemistry, as well as the mechanisms enabling it to colonize and cause disease in various hosts, are not well understood, and new approaches are required to understand its basic biology. High-throughput sequencing technologies provide unprecedented opportunities for functional genomic research. Recent studies have shown that direct Illumina sequencing of cDNA (RNA-seq) is a useful technique for the quantitative and qualitative examination of transcriptomes. In this study we report RNA-seq analyses of the transcriptomes of C. jejuni (NCTC11168) and its rpoN mutant. This has allowed the identification of hitherto unknown transcriptional units, and further defines the regulon that is dependent on rpoN for expression. The analysis of the NCTC11168 transcriptome was supplemented by additional proteomic analysis using liquid chromatography-MS. The transcriptomic and proteomic datasets represent an important resource for the Campylobacter research community.
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Mattei, Daniele, Andranik Ivanov, Marc van Oostrum, Stanislav Pantelyushin, Juliet Richetto, Flavia Mueller, Michal Beffinger, et al. "Enzymatic Dissociation Induces Transcriptional and Proteotype Bias in Brain Cell Populations." International Journal of Molecular Sciences 21, no. 21 (October 26, 2020): 7944. http://dx.doi.org/10.3390/ijms21217944.
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Different cell isolation techniques exist for transcriptomic and proteotype profiling of brain cells. Here, we provide a systematic investigation of the influence of different cell isolation protocols on transcriptional and proteotype profiles in mouse brain tissue by taking into account single-cell transcriptomics of brain cells, proteotypes of microglia and astrocytes, and flow cytometric analysis of microglia. We show that standard enzymatic digestion of brain tissue at 37 °C induces profound and consistent alterations in the transcriptome and proteotype of neuronal and glial cells, as compared to an optimized mechanical dissociation protocol at 4 °C. These findings emphasize the risk of introducing technical biases and biological artifacts when implementing enzymatic digestion-based isolation methods for brain cell analyses.
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Thompson, Ammon, Michael R. May, Brian R. Moore, and Artyom Kopp. "A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes." Proceedings of the National Academy of Sciences 117, no. 32 (July 24, 2020): 19339–46. http://dx.doi.org/10.1073/pnas.1919748117.
Full textAbstract:
Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets—where we demonstrate its ability to correctly infer true (known) expression states—and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-sequencing (RNA-seq) datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely available R package zigzag.
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Smith, Meghan, Gabriella McWilliams, Angela Bryan, Douglas Seals, and Thomas LaRocca. "Total Transcriptome Responses to Low and Higher Intensity Aerobic Exercise Interventions in Older Adults." Innovation in Aging 5, Supplement_1 (December 1, 2021): 683. http://dx.doi.org/10.1093/geroni/igab046.2569.
Full textAbstract:
Abstract Aerobic exercise is a universally recommended strategy for increasing healthspan, and recent advances in next-generation sequencing and bioinformatics (e.g., RNA-seq/transcriptomics) have made it possible to broadly profile the molecular transducers of exercise. However, most transcriptome studies of exercise have focused on coding genes only, and the transcriptomic response to different exercise interventions has not been characterized by RNA-seq in older adults. Therefore, we performed total RNA-seq (to capture both coding and non-coding gene expression) on peripheral blood mononuclear cells collected from healthy, previously sedentary older adults (males and females, aged 70 ± 1 years). Samples were collected before and after 16 weeks of either low-intensity continuous training (LICT, 50% maximum heart rate, 3 x 30 min/week) or moderate-intensity continuous training plus interval training (MICT+IT, 60-80% maximum heart rate, progressively increased to include IT, 3 x 30 min/week). We found that both interventions modified biological processes (transcriptome modules) related to oxygen transport and reduced inflammatory signaling/immune activation processes (more pronounced with LICT). Interestingly, transcriptome changes unique to LICT subjects included increased expression of genes linked with vascularization and endothelial cell migration, whereas MICT+IT was uniquely associated with a robust increase in antioxidant response gene expression. We also observed numerous changes in long non-coding RNAs and microRNAs that could be linked with these exercise-associated gene expression changes with both interventions. These data provide a first comprehensive look into transcriptomic changes associated with moderate vs. low intensity aerobic exercise in older adults, and they suggest distinct benefits of each exercise strategy.
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Ahn, Antonio, Euan J. Rodger, Jyoti Motwani, Gregory Gimenez, Peter A. Stockwell, Matthew Parry, Peter Hersey, Aniruddha Chatterjee, and Michael R. Eccles. "Transcriptional Reprogramming and Constitutive PD-L1 Expression in Melanoma Are Associated with Dedifferentiation and Activation of Interferon and Tumour Necrosis Factor Signalling Pathways." Cancers 13, no. 17 (August 24, 2021): 4250. http://dx.doi.org/10.3390/cancers13174250.
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Melanoma is the most aggressive type of skin cancer, with increasing incidence worldwide. Advances in targeted therapy and immunotherapy have improved the survival of melanoma patients experiencing recurrent disease, but unfortunately treatment resistance frequently reduces patient survival. Resistance to targeted therapy is associated with transcriptomic changes and has also been shown to be accompanied by increased expression of programmed death ligand 1 (PD-L1), a potent inhibitor of immune response. Intrinsic upregulation of PD-L1 is associated with genome-wide DNA hypomethylation and widespread alterations in gene expression in melanoma cell lines. However, an in-depth analysis of the transcriptomic landscape of melanoma cells with intrinsically upregulated PD-L1 expression is lacking. To determine the transcriptomic landscape of intrinsically upregulated PD-L1 expression in melanoma, we investigated transcriptomes in melanomas with constitutive versus inducible PD-L1 expression (referred to as PD-L1CON and PD-L1IND). RNA-Seq analysis was performed on seven PD-L1CON melanoma cell lines and ten melanoma cell lines with low inducible PD-L1IND expression. We observed that PD-L1CON melanoma cells had a reprogrammed transcriptome with a characteristic pattern of dedifferentiated gene expression, together with active interferon (IFN) and tumour necrosis factor (TNF) signalling pathways. Furthermore, we identified key transcription factors that were also differentially expressed in PD-L1CON versus PD-L1IND melanoma cell lines. Overall, our studies describe transcriptomic reprogramming of melanomas with PD-L1CON expression.
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Dovrou, Aikaterini, Ekaterini Bei, Stelios Sfakianakis, Kostas Marias, Nickolas Papanikolaou, and Michalis Zervakis. "Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study." Diagnostics 13, no. 4 (February 15, 2023): 738. http://dx.doi.org/10.3390/diagnostics13040738.
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Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC.
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Chen, Ce, Yining Ge, and Lingli Lu. "Opportunities and challenges in the application of single-cell and spatial transcriptomics in plants." Frontiers in Plant Science 14 (August 11, 2023). http://dx.doi.org/10.3389/fpls.2023.1185377.
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Single-cell and spatial transcriptomics have diverted researchers’ attention from the multicellular level to the single-cell level and spatial information. Single-cell transcriptomes provide insights into the transcriptome at the single-cell level, whereas spatial transcriptomes help preserve spatial information. Although these two omics technologies are helpful and mature, further research is needed to ensure their widespread applicability in plant studies. Reviewing recent research on plant single-cell or spatial transcriptomics, we compared the different experimental methods used in various plants. The limitations and challenges are clear for both single-cell and spatial transcriptomic analyses, such as the lack of applicability, spatial information, or high resolution. Subsequently, we put forth further applications, such as cross-species analysis of roots at the single-cell level and the idea that single-cell transcriptome analysis needs to be combined with other omics analyses to achieve superiority over individual omics analyses. Overall, the results of this review suggest that combining single-cell transcriptomics, spatial transcriptomics, and spatial element distribution can provide a promising research direction, particularly for plant research.
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Krishnan, Parvathy, Celine Caseys, Nik Soltis, Wei Zhang, Meike Burow, and Daniel J. Kliebenstein. "Polygenic pathogen networks influence transcriptional plasticity in the Arabidopsis-Botrytis pathosystem." GENETICS, May 22, 2023. http://dx.doi.org/10.1093/genetics/iyad099.
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Abstract Bidirectional flow of information shapes the outcome of the host-pathogen interactions and depends on the genetics of each organism. Recent work has begun to use co-transcriptomic studies to shed light on this bidirectional flow, but it is unclear how plastic the co-transcriptome is in response to genetic variation in both the host and pathogen. To study co-transcriptome plasticity, we conducted transcriptomics using natural genetic variation in the pathogen, Botrytis cinerea, and large effect genetic variation abolishing defense signaling pathways within the host, Arabidopsis thaliana. We show that genetic variation in the pathogen has a greater influence on the co-transcriptome than mutations that abolish defense signaling pathways in the host. Genome wide association mapping using the pathogens genetic variation and both organisms’ transcriptomes allowed an assessment of how the pathogen modulates plasticity in response to the host. This showed that the differences in both organism’s responses were linked to trans-eQTL hotspots within the pathogen’s genome. These hotspots control gene sets in either the host or pathogen and show differential allele sensitivity to the hosts genetic variation rather than qualitative host specificity. Interestingly, nearly all the trans-eQTL hotspots were unique to the host or pathogen transcriptomes. In this system of differential plasticity, the pathogen mediates the shift in the co-transcriptome more than the host.
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Kucharski, Michal, Jaishree Tripathi, Sourav Nayak, Lei Zhu, Grennady Wirjanata, Rob W. van der Pluijm, Mehul Dhorda, Arjen Dondorp, and Zbynek Bozdech. "A comprehensive RNA handling and transcriptomics guide for high-throughput processing of Plasmodium blood-stage samples." Malaria Journal 19, no. 1 (October 9, 2020). http://dx.doi.org/10.1186/s12936-020-03436-w.
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Abstract Background Sequencing technology advancements opened new opportunities to use transcriptomics for studying malaria pathology and epidemiology. Even though in recent years the study of whole parasite transcriptome proved to be essential in understanding parasite biology there is no compiled up-to-date reference protocol for the efficient generation of transcriptome data from growing number of samples. Here, a comprehensive methodology on how to preserve, extract, amplify, and sequence full-length mRNA transcripts from Plasmodium-infected blood samples is presented that can be fully streamlined for high-throughput studies. Results The utility of various commercially available RNA-preserving reagents in a range of storage conditions was evaluated. Similarly, several RNA extraction protocols were compared and the one most suitable method for the extraction of high-quality total RNA from low-parasitaemia and low-volume blood samples was established. Furthermore, the criteria needed to evaluate the quality and integrity of Plasmodium RNA in the presence of human RNA was updated. Optimization of SMART-seq2 amplification method to better suit AT-rich Plasmodium falciparum RNA samples allowed us to generate high-quality transcriptomes from as little as 10 ng of total RNA and a lower parasitaemia limit of 0.05%. Finally, a modified method for depletion of unwanted human haemoglobin transcripts using in vitro CRISPR-Cas9 treatment was designed, thus improving parasite transcriptome coverage in low parasitaemia samples. To prove the functionality of the pipeline for both laboratory and field strains, the highest 2-hour resolution RNA-seq transcriptome for P. falciparum 3D7 intraerythrocytic life cycle available to date was generated, and the entire protocol was applied to create the largest transcriptome data from Southeast Asian field isolates. Conclusions Overall, the presented methodology is an inclusive pipeline for generation of good quality transcriptomic data from a diverse range of Plasmodium-infected blood samples with varying parasitaemia and RNA inputs. The flexibility of this pipeline to be adapted to robotic handling will facilitate both small and large-scale future transcriptomic studies in the field of malaria.
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Yoo, Taesun, Ye-Eun Yoo, Hyojin Kang, and Eunjoon Kim. "Age, brain region, and gene dosage-differential transcriptomic changes in Shank3-mutant mice." Frontiers in Molecular Neuroscience 15 (October 12, 2022). http://dx.doi.org/10.3389/fnmol.2022.1017512.
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Shank3 is an abundant excitatory postsynaptic scaffolding protein implicated in various neurodevelopmental disorders, including autism spectrum disorder (ASD), Phelan-McDermid syndrome, intellectual disability, and schizophrenia. Shank3-mutant mice show various molecular, synaptic, and behavioral deficits, but little is known about how transcriptomic phenotypes vary across different ages, brain regions, and gene dosages. Here, we report transcriptomic patterns in the forebrains of juvenile and adult homozygous Shank3-mutant mice that lack exons 14–16 and also the prefrontal, hippocampal, and striatal transcriptomes in adult heterozygous and homozygous Shank3-mutant mice. The juvenile and adult mutant transcriptomes show patterns opposite from and similar to those observed in ASD (termed reverse-ASD and ASD-like patterns), respectively. The juvenile transcriptomic changes accompany synaptic upregulations and ribosomal and mitochondrial downregulations, whereas the adult transcriptome show opposite changes. The prefrontal, hippocampal, and striatal transcriptomes show differential changes in ASD-related gene expressions and biological functions associated with synapse, ribosome, mitochondria, and spliceosome. These patterns also differ across heterozygous and homozygous Shank3-mutant mice. These results suggest age, brain region, and gene dosage-differential transcriptomic changes in Shank3-mutant mice.
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Rocque, Brittany, Kate Guion, Pranay Singh, Sarah Bangerth, Lauren Pickard, Jashdeep Bhattacharjee, Sofia Eguizabal, et al. "Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease." Scientific Reports 14, no. 1 (February 13, 2024). http://dx.doi.org/10.1038/s41598-024-53993-2.
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AbstractSingle cell and spatially resolved ‘omic’ techniques have enabled deep characterization of clinical pathologies that remain poorly understood, providing unprecedented insights into molecular mechanisms of disease. However, transcriptomic platforms are costly, limiting sample size, which increases the possibility of pre-analytical variables such as tissue processing and storage procedures impacting RNA quality and downstream analyses. Furthermore, spatial transcriptomics have not yet reached single cell resolution, leading to the development of multiple deconvolution methods to predict individual cell types within each transcriptome ‘spot’ on tissue sections. In this study, we performed spatial transcriptomics and single nucleus RNA sequencing (snRNAseq) on matched specimens from patients with either histologically normal or advanced fibrosis to establish important aspects of tissue handling, data processing, and downstream analyses of biobanked liver samples. We observed that tissue preservation technique impacts transcriptomic data, especially in fibrotic liver. Single cell mapping of the spatial transcriptome using paired snRNAseq data generated a spatially resolved, single cell dataset with 24 unique liver cell phenotypes. We determined that cell–cell interactions predicted using ligand–receptor analysis of snRNAseq data poorly correlated with cellular relationships identified using spatial transcriptomics. Our study provides a framework for generating spatially resolved, single cell datasets to study gene expression and cell–cell interactions in biobanked clinical samples with advanced liver disease.
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Briggs, Emma M., Catarina A. Marques, Guy R. Oldrieve, Jihua Hu, Thomas D. Otto, and Keith R. Matthews. "Profiling the bloodstream form and procyclic form Trypanosoma brucei cell cycle using single cell transcriptomics." eLife 12 (May 11, 2023). http://dx.doi.org/10.7554/elife.86325.
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African trypanosomes proliferate as bloodstream forms and procyclic forms in the mammal and tsetse fly midgut, respectively. This allows them to colonise the host environment upon infection and ensure life cycle progression. Yet, understanding of the mechanisms that regulate and drive the cell replication cycle of these forms is limited. Using single cell transcriptomics on unsynchronised cell populations, we have obtained high resolution cell cycle regulated transcriptomes of both procyclic and slender bloodstream form Trypanosoma brucei without prior cell sorting or synchronisation. Additionally, we describe an efficient freeze-thawing protocol that allows single cell transcriptomic analysis of cryopreserved T. brucei. Computational reconstruction of the cell cycle using periodic pseudotime inference allowed the dynamic expression patterns of cycling genes to be profiled for both life cycle forms. Comparative analyses identify a core cycling transcriptome highly conserved between forms, as well as several genes where transcript levels dynamics are form-specific. Comparing transcript expression patterns with protein abundance revealed that the majority of genes with periodic cycling transcript and protein levels exhibit a relative delay between peak transcript and protein expression. This work reveals novel detail of the cell cycle regulated transcriptomes of both forms, which are available for further interrogation via an interactive webtool.
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Cerapio, Juan Pablo, Pauline Gravelle, Anne Quillet‐Mary, Carine Valle, Frederic Martins, Don‐Marc Franchini, Charlotte Syrykh, et al. "Integrated spatial and multimodal single‐cell transcriptomics reveal patient‐dependent cell heterogeneity in splenic marginal zone lymphoma." Journal of Pathology, June 3, 2024. http://dx.doi.org/10.1002/path.6296.
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AbstractBiological hallmarks of splenic marginal zone lymphoma (SMZL) remain poorly described. Herein, we performed in‐depth SMZL characterization through multimodal single‐cell analyses of paired blood/spleen samples. The 3’‐single‐cell RNA‐sequencing, Cellular Indexing of Transcriptomes and Epitopes by sequencing, and 5’‐V(D)J single‐cell RNA‐sequencing datasets were integrated to characterize SMZL transcriptome profiles, including B‐cell receptor and T‐cell receptor repertoires. Hyperexpanded B‐cell clones in the spleen were at a memory‐like stage, whereas recirculating tumor B‐cells in blood encompassed multiple differentiation stages, indicating an unexpected desynchronization of the B‐cell maturation program in SMZL cells. Spatial transcriptomics showed the enrichment of T‐effector and T‐follicular helper (TFH) signatures in the nodular subtype of SMZL. This latter also exhibited gene‐based cell–cell interactions suggestive of dynamic crosstalk between TFH and cancer cells in transcriptomics, further substantiated by using imaging mass cytometry. Our findings provide a comprehensive high‐resolution description of SMZL biological hallmarks and characterize, for the first time in situ, inter‐ and intra‐patient heterogeneity at both transcriptomic and protein levels. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Gómez-Godínez, Lorena Jacqueline, Carlos Iván Cruz-Cárdenas, Edith Rojas-Anaya, Marco Aurelio Aragón-Magadan, and Luis Felipe Guzmán. "ENFOQUES GENÓMICOS Y TRANSCRIPTÓMICOS PARA ESTUDIAR ÁRBOLES MADERABLES: PERSPECTIVAS PARA EL ESTUDIO DE CEDRO ROJO (Cedrela odorata L.)." Tropical and Subtropical Agroecosystems 27, no. 1 (November 13, 2023). http://dx.doi.org/10.56369/tsaes.4773.
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<p><strong>Introduction</strong>: The high-throughput genomic and transcriptomic approach has been developed and implemented to address the main challenges that the timber forestry sector faces, such as population growth, climate change, deforestation and the loss of forest ecosystem services. <strong>Objective:</strong> To carry out a bibliographic review focused on the genomes and transcriptomes of timber trees reported in the databases, with special attention to red cedar (<em>Cedrela odorata</em> L.), due to its importance as precious wood in Mexico. <strong>Methodology:</strong> A literature review was carried out, directed at studying timber trees with genomic and transcriptomic strategies in different databases such as Pubmed, Scopus, Google Scholar, ScienceDirect, Wiley Online Library, MDPI and Scielo to identify the timber species that have been reported genomes and transcriptomes. The structure of the review was the genomics of timber trees, the transcriptomics of wood, and the potential species for study due to their importance and finally, the databases for consultation. Subsequently, a bibliometric study was carried out with the bibliometrix library in R Studio. <strong>Main results</strong>: The first genome to be assembled at the chromosome level was the black cottonwood. Among the timber trees, the genomes of black cottonwood, desert poplar, eucalyptus and oak with a length of 392, 496, 691 and 789 Mb have been reported. Through study of the transcriptome, it has been possible to identify genes related to formation of the wood in a hybrid poplar (<em>Populus alba</em> L.<em> × P. glandulosa</em>) and <em>P. tremula</em> L. and with drought tolerance in <em>Pinus massoniana</em> and <em>Pinus pinaster</em> Aiton. In red cedar (<em>Cedrela odorata</em> L.), the transcriptome was obtained by sequencing a single leaf, identifying 52,181 gene models. In the NCBI, EMBL-EBI, TreeGenes, PLAZA databases and the hardwood genomics website it is possible to find information related to the genomics and transcriptomics of timber species. <strong>Implications:</strong> More research is required in omics in timber, particularly in red cedar, since the search on these topics yielded little information. <strong>Conclusion</strong>: Through the bibliographic review in databases, the timber trees that have a described genome and transcriptome were identified. This information can be used for the assembly and annotation of new genomes, identification of genes and molecular markers, among other applications.</p>
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Pont, Frédéric, Juan Pablo Cerapio, Pauline Gravelle, Laetitia Ligat, Carine Valle, Emeline Sarot, Marion Perrier, et al. "Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics." BMC Bioinformatics 24, no. 1 (January 27, 2023). http://dx.doi.org/10.1186/s12859-023-05150-1.
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Abstract Background: The development of single-cell technologies yields large datasets of information as diverse and multimodal as transcriptomes, immunophenotypes, and spatial position from tissue sections in the so-called ’spatial transcriptomics’. Currently however, user-friendly, powerful, and free algorithmic tools for straightforward analysis of spatial transcriptomic datasets are scarce. Results: Here, we introduce Single-Cell Spatial Explorer, an open-source software for multimodal exploration of spatial transcriptomics, examplified with 9 human and murine tissues datasets from 4 different technologies. Conclusions: Single-Cell Spatial Explorer is a very powerful, versatile, and interoperable tool for spatial transcriptomics analysis.