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A limited set of transcriptional programs define major cell types.

ABSTRACT: We have produced RNA sequencing data for 53 primary cells from different locations in the human body. The clustering of these primary cells reveals that most cells in the human body share a few broad transcriptional programs, which define five major cell types: epithelial, endothelial, mesenchymal, neural, and blood cells. These act as basic components of many tissues and organs. Based on gene expression, these cell types redefine the basic histological types by which tissues have been traditionally classified. We identified genes whose expression is specific to these cell types, and from these genes, we estimated the contribution of the major cell types to the composition of human tissues. We found this cellular composition to be a characteristic signature of tissues and to reflect tissue morphological heterogeneity and histology. We identified changes in cellular composition in different tissues associated with age and sex, and found that departures from the normal cellular composition correlate with histological phenotypes associated with disease.

SUBMITTER: Breschi A

PROVIDER: S-EPMC7397875 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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A limited set of transcriptional programs define major cell types.

Breschi Alessandra A Muñoz-Aguirre Manuel M Wucher Valentin V Davis Carrie A CA Garrido-Martín Diego D Djebali Sarah S Gillis Jesse J Pervouchine Dmitri D DD Vlasova Anna A Dobin Alexander A Zaleski Chris C Drenkow Jorg J Danyko Cassidy C Scavelli Alexandra A Reverter Ferran F Snyder Michael P MP Gingeras Thomas R TR Guigó Roderic R

Genome research 20200729 7

We have produced RNA sequencing data for 53 primary cells from different locations in the human body. The clustering of these primary cells reveals that most cells in the human body share a few broad transcriptional programs, which define five major cell types: epithelial, endothelial, mesenchymal, neural, and blood cells. These act as basic components of many tissues and organs. Based on gene expression, these cell types redefine the basic histological types by which tissues have been tradition ...[more]

PMID: 32759341

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Project description:BackgroundEpithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.MethodsNon-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.ResultsApplication of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.ConclusionsThis robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

Project description:BackgroundKinases mTORC1 and AMPK act as energy sensors, controlling nutrient responses and cellular growth. Changes in nutrient levels affect diverse transcriptional networks, making it challenging to identify downstream paths that regulate cellular growth or a switch to development via nutrient variation. The life cycle of Dictyostelium presents an excellent model to study the mTORC1 signaling function for growth and development. Dictyostelium grow as single cells in nutrient-rich media, but, upon nutrient withdrawal, growth ceases and cells enter a program for multi-cell development. While nearly half the genome shows gene expression changes upon nutrient removal, we hypothesized that not all of these genes are required for the switch to program development. Through manipulation of mTORC1 activity alone, without nutrient removal, we focused on a core network of genes that are required for switching between growth and development for regulation of cell fate decisions.ResultsTo identify developmentally essential genes, we sought ways to promote development in the absence of nutrient loss. We first examined the activities of mTORC1 and AMPK in Dictyostelium during phases of rapid growth and starvation-induced development and showed they exhibited reciprocal patterns of regulation under various conditions. Using these as initial readouts, we identified rich media conditions that promoted rapid cell growth but, upon mTORC1 inactivation by rapamycin, led to a growth/development switch. Examination of gene expression during cell fate switching showed that changes in expression of most starvation-regulated genes were not required for developmental induction. Approximately 1000 genes which become downregulated upon rapamycin treatment comprise a cellular growth network involving ribosome biogenesis, protein synthesis, and cell cycle processes. Conversely, the upregulation of ~ 500 genes by rapamycin treatment defines essential signaling pathways for developmental induction, and ~ 135 of their protein products intersect through the well-defined cAMP/PKA network. Many of the rapamycin-induced genes we found are currently unclassified, and mutation analyses of 5 such genes suggest a novel gene class essential for developmental regulation.ConclusionsWe show that manipulating activities of mTORC1/AMPK in the absence of nutrient withdrawal is sufficient for a growth-to-developmental fate switch in Dictyostelium, providing a means to identify transcriptional networks and signaling pathways essential for early development.

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A limited set of transcriptional programs define major cell types.

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A limited set of transcriptional programs define major cell types.

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