SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer.

Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.

Single-cell network analysis reveals gene expression programs for Arabidopsis root development and metabolism.

Single-cell RNA-sequencing datasets of Arabidopsis roots have been generated, but related comprehensive gene co-expression network analyses are lacking. We conducted a single-cell gene co-expression network analysis with publicly available scRNA-seq datasets of Arabidopsis roots using a SingleCellGGM algorithm. The analysis identified 149 gene co-expression modules, which we considered to be gene expression programs (GEPs). By examining their spatiotemporal expression, we identified GEPs specifically expressed in major root cell types along their developmental trajectories. These GEPs define gene programs regulating root cell development at different stages and are enriched with relevant developmental regulators. As examples, a GEP specific for the quiescent center (QC) contains 20 genes regulating QC and stem cell niche homeostasis, and four GEPs are expressed in sieve elements (SEs) from early to late developmental stages, with the early-stage GEP containing 17 known SE developmental regulators. We also identified GEPs for metabolic pathways with cell-type-specific expression, suggesting the existence of cell-type-specific metabolism in roots. Using the GEPs, we discovered and verified a columella-specific gene, NRL27, as a regulator of the auxin-related root gravitropism response. Our analysis thus systematically reveals GEPs that regulate Arabidopsis root development and metabolism and provides ample resources for root biology studies.

A specific and portable gene expression program underlies antigen archiving by lymphatic endothelial cells.

Antigens from protein subunit vaccination traffic from the tissue to the draining lymph node, either passively via the lymph or carried by dendritic cells at the local injection site. Lymph node (LN) lymphatic endothelial cells (LEC) actively acquire and archive foreign antigens, and archived antigen can be released during subsequent inflammatory stimulus to improve immune responses. Here, we answer questions about how LECs achieve durable antigen archiving and whether there are transcriptional signatures associated with LECs containing high levels of antigen. We used single cell sequencing in dissociated LN tissue to quantify antigen levels in LEC and dendritic cell populations at multiple timepoints after immunization, and used machine learning to define a unique transcriptional program within archiving LECs that can predict LEC archiving capacity in independent data sets. Finally, we validated this modeling, showing we could predict antigen archiving from a transcriptional dataset of CHIKV infected mice and demonstrated in vivo the accuracy of our prediction. Collectively, our findings establish a unique transcriptional program in LECs that promotes antigen archiving that can be translated to other systems.

The MYBL1/TCFL5 transcription network: two collaborative factors with central role in male meiosis.

Male gametogenesis, spermatogenesis, is a stepwise developmental process to generate mature sperm. The most intricate process of spermatogenesis is meiosis during which two successive cell divisions ensue with dramatic cellular and molecular changes to produce haploid cells. After entry into meiosis, several forms of regulatory events control the orderly progression of meiosis and the timely entry into post-meiotic sperm differentiation. Among other mechanisms, changes to gene expression are controlled by key transcription factors. In this review, we will discuss the gene regulatory mechanisms underlying meiotic entry, meiotic progression, and post-meiotic differentiation with a particular emphasis on the MYBL1/TCFL5 regulatory architecture and how this architecture involves in various forms of transcription network motifs to regulate gene expression.

Multi-modular structure of the gene regulatory network for specification and commitment of murine T cells.

T cells develop from multipotent progenitors by a gradual process dependent on intrathymic Notch signaling and coupled with extensive proliferation. The stages leading them to T-cell lineage commitment are well characterized by single-cell and bulk RNA analyses of sorted populations and by direct measurements of precursor-product relationships. This process depends not only on Notch signaling but also on multiple transcription factors, some associated with stemness and multipotency, some with alternative lineages, and others associated with T-cell fate. These factors interact in opposing or semi-independent T cell gene regulatory network (GRN) subcircuits that are increasingly well defined. A newly comprehensive picture of this network has emerged. Importantly, because key factors in the GRN can bind to markedly different genomic sites at one stage than they do at other stages, the genes they significantly regulate are also stage-specific. Global transcriptome analyses of perturbations have revealed an underlying modular structure to the T-cell commitment GRN, separating decisions to lose "stem-ness" from decisions to block alternative fates. Finally, the updated network sheds light on the intimate relationship between the T-cell program, which depends on the thymus, and the innate lymphoid cell (ILC) program, which does not.

Decoding a gene expression program that accompanies the phenotype of sporadic and basal cell nevus syndrome-associated odontogenic keratocyst.

BACKGROUND: Odontogenic keratocyst is characterized by local aggressive behavior and a high recurrence rate, as well as its potential to develop in association with the basal cell nevus syndrome. The aim of this study was to decode the gene expression program accompanying odontogenic keratocyst phenotype. METHODS: 150-bp paired-end RNA-sequencing was applied on six sporadic and six basal cell nevus syndrome-associated whole-tissue odontogenic keratocyst samples in comparison to six dental follicles, coupled with bioinformatics and complemented by immunohistochemistry. RESULTS: 2654 and 2427 differentially expressed genes were captured to characterize the transcriptome of sporadic and basal cell nevus syndrome-associated odontogenic keratocysts, respectively. Gene ontologies related to "epidermis/skin development" and "keratinocyte/epidermal cell differentiation" were enriched among the upregulated genes (KRT10, NCCRP1, TP63, GRHL3, SOX21), while "extracellular matrix organization" (ITGA5, LOXL2) and "odontogenesis" (MSX1, LHX8) gene ontologies were overrepresented among the downregulated genes in odontogenic keratocyst. Interestingly, upregulation of various embryonic stem cells markers (EPHA1, SCNN1A) and genes committed in cellular reprogramming (SOX2, KLF4, OVOL1, IRF6, TACSTD2, CDH1) was found in odontogenic keratocyst. These findings were highly shared between sporadic and basal cell nevus syndrome-associated odontogenic keratocysts. Immunohistochemistry verified SOX2, KLF4, OVOL1, IRF6, TACSTD2/TROP2, CDH1/E-cadherin, and p63 expression predominantly in the odontogenic keratocyst suprabasal epithelial layers. CONCLUSION: The odontogenic keratocyst transcriptomic profile is characterized by a prominent epidermal and dental epithelial fate, a repressed dental mesenchyme fate combined with deregulated extracellular matrix organization, and enhanced stemness gene signatures. Thus, we propose a developed epidermis-like phenotype in the odontogenic keratocyst suprabasal epithelial cells, established in parallel to a significant upregulation of marker genes related to embryonic stem cells and cellular reprogramming.

A critical analysis on the conception of "Pre-existent gene expression programs" for cell differentiation and development.

A pre-existent gene expression program at the basis of cell differentiation and development is often assumed in the current scientific literature. Historically this conception is traced to the nineteen sixties of the last century, when various influential papers and scientific personalities imprinted their view drawing inspiration from informatics. The accepted model is that in the presence of certain external and/or internal signals, a cell initiates a pre-determined program of gene expression by which it becomes differentiated. Authors generally do not question the evidence for the existence of such a program. Here I review different aspects and consequences of this model to conclude that it is completely at odds with the literature of the last decades, which has given us a splendid view of the dynamics of the living cell as an auto-organizing complex unit that is far away from thermodynamical equilibrium. In this view there is no place for programs.

Mapping single-cell atlases throughout Metazoa unravels cell type evolution.

Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning sponge to mouse, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

Gene expression signatures of site-specificity in cancer metastases.

We have previously shown that metastases are generally characterized by a core program of gene expression that induces the oxidative energy metabolism, activates vascularization/tissue remodeling, silences extracellular matrix interactions, and alters ion homeostasis. This core program distinguishes metastases from their originating primary tumors as well as from their target host tissues. We hypothesized that organ preference is reflected in additional, site-selective components within the metastatic gene expression programs. Expanding our prior analysis of 653 human gene expression profiles plus data from a murine model, we find that the release from the primary tumor is associated with a suppression of functions that are important for the identity of the organ of origin, such as a down-regulation of steroid hormone responsiveness in the disseminated foci derived from prostate cancer. Metastases adjust to their target microenvironment by up-regulating-even overexpressing-genes and genetic programs that are characteristic of that organ. Finally, alterations in RNA and protein processing as well as immune deviation are common. In the clinic, metastases are mostly treated with the chemotherapy protocols devised for their primary tumors. Adjustments that account for the gene expression differences between primary and metastatic cancers have the potential to improve the currently dismal success rates.

Control of a neuronal morphology program by an RNA-binding zinc finger protein, Unkempt.

Cellular morphology is an essential determinant of cellular function in all kingdoms of life, yet little is known about how cell shape is controlled. Here we describe a molecular program that controls the early morphology of neurons through a metazoan-specific zinc finger protein, Unkempt. Depletion of Unkempt in mouse embryos disrupts the shape of migrating neurons, while ectopic expression confers neuronal-like morphology to cells of different nonneuronal lineages. We found that Unkempt is a sequence-specific RNA-binding protein and identified its precise binding sites within coding regions of mRNAs linked to protein metabolism and trafficking. RNA binding is required for Unkempt-induced remodeling of cellular shape and is directly coupled to a reduced production of the encoded proteins. These findings link post-transcriptional regulation of gene expression with cellular shape and have general implications for the development and disease of multicellular organisms.

Integrating non-coding RNAs in JAK-STAT regulatory networks.

Being a well-characterized pathway, JAK-STAT signaling serves as a valuable paradigm for studying the architecture of gene regulatory networks. The discovery of untranslated or non-coding RNAs, namely microRNAs and long non-coding RNAs, provides an opportunity to elucidate their roles in such networks. In principle, these regulatory RNAs can act as downstream effectors of the JAK-STAT pathway and/or affect signaling by regulating the expression of JAK-STAT components. Examples of interactions between signaling pathways and non-coding RNAs have already emerged in basic cell biology and human diseases such as cancer, and can potentially guide the identification of novel biomarkers or drug targets for medicine.