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The Drosophila visual system supports complex behaviors and shares many of its anatomical and molecular features with the vertebrate brain. Yet, it contains a much more manageable number of neurons and neuronal types. In addition to the extensive Drosophila genetic toolbox, this relative simplicity has allowed decades of work to yield a detailed account of its neuronal type diversity, morphology, connectivity and specification mechanisms. In the past three years, numerous studies have applied large scale single-cell transcriptomic approaches to the Drosophila visual system and have provided access to the complete gene expression profile of most neuronal types throughout development. This makes the fly visual system particularly well suited to perform detailed studies of the genetic mechanisms underlying the evolution and development of neuronal systems. Here, we highlight how these transcriptomic resources allow exploring long-standing biological questions under a new light. We first present the efforts made to characterize neuronal diversity in the Drosophila visual system and suggest ways to further improve this description. We then discuss current advances allowed by the single-cell datasets, and envisage how these datasets can be further leveraged to address fundamental questions regarding the regulation of neuronal identity, neuronal circuit development and the evolution of neuronal diversity.

Paramount to the survival of animals with a closed circulatory system is the regulation of blood pressure and blood flow to critical tissues. This is accomplished by the renin-angiotensin system (RAS), an enzymatic cascade that culminates in the production of Angiotensin II, a potent vasoconstrictor that controls blood pressure, renal hemodynamics and fluid-electrolyte homeostasis. Across millions of years of development, the RAS has been perfected throughout evolution to enable animals to survive fluctuations in fluid-electrolyte balance and blood pressure. The key event in the RAS and its rate limiting step is the tightly regulated synthesis and secretion of renin, a hormone-enzyme which cleaves angiotensinogen into Angiotensin I which is further converted to Angiotensin II by Angiotensin Converting Enzyme (ACE). Under normal physiologic conditions, renin is secreted by juxtaglomerular (JG) cells located in the walls of the afferent arterioles at the entrance to the glomeruli of the kidney. Although the nominal number of JG cells is small, representing 0.1-0.01% of all kidney cells4, their production of renin is, under normal conditions, typically sufficient to sustain blood pressure and ensure survival. However, during intense threats to homeostasis by hypotension, dehydration or administration of RAS inhibitors, mesangial cells, smooth muscle cells and pericytes along the afferent arterioles transform into renin expressing cells – a process described as “recruitment”. This process is a result of direct transdifferentiation of the aforementioned cell types in the kidney. These cells undergo a remarkable switch in cellular identity to express renin and other genes crucial to attain the renin cell program before presumably returning to their original identity once the threat abates and homeostasis is restored. The underlying mechanisms which bestow this rare and fascinating ability upon these cells remain unknown and are explored in this thesis.

Understanding how complex cell-fate decisions emerge at the molecular level is a key challenge in developmental biology. Despite remarkable progress in decoding the contribution of the linear epigenome, how spatial genome architecture functionally informs changes in gene expression remains unclear. In this review, we discuss recent insights in elucidating the molecular landscape of genome folding, emphasizing the multilayered nature of the 3D genome, its importance for gene regulation, and its spatiotemporal dynamics. Finally, we discuss how these new concepts and emergent technologies will enable us to address some of the outstanding questions in development and disease.

Retrotransposons are genetic elements present across all eukaryotic genomes. While their role in evolution is considered as a potentially beneficial natural source of genetic variation, their activity is classically considered detrimental due to their potentially harmful effects on genome stability. However, studies are increasingly shedding light on the regulatory function and beneficial role of somatic retroelement reactivation in non-pathological contexts. Here, we review recent findings unveiling the regulatory potential of retrotransposons, including their role in noncoding RNA transcription, as modulators of mammalian transcriptional and epigenome landscapes. We also discuss technical challenges in deciphering the multifaceted activity of retrotransposable elements, highlighting an unforeseen central role of this neglected portion of the genome both in early development and in adult life.

The aim of this editorial is to examine if and why a new journal focusing on issues concerning identity in general, cell identity, or cell-group identity could offer a new perspective or better vision in cell science. Recent advancements in technology (single-cell -omics) and the high amount of data and perspectives provided by the former experience in other domains of science (mainly social sciences) could offer at least some answers and, more importantly, allow new questions in biology and life sciences. Issues such as diversity, identity management in cells, and conservation versus change of identity, reprogramming, identity control, or identity recognition, may be interpreted or analyzed in a more complex manner if regarded form the perspective of the concept of identity. Deciphering what mechanisms stabilize or regulate cell identity could be crucial in understanding cell behavior. Learning how different pathogens or transformed cells hijack mechanisms involved in maintaining cell identity may offer, from a practical point of view, models or instruments to imagine means of control and maintain or manipulate cell identity during development, physiology, or disease. This opening article in the Journal of Cell Identity briefly mentions some of the emerging ideas concerning cell identity and is intended as a starting point for debate and analysis of more aspects concerning cell identity in health and disease.

A neural clock controls what we do each day, and understanding its circuitry is important for health. In this issue of Neuron, Shan et al. visualize molecular rhythms in subtypes of master clock neurons to test principles of cell identity and network wiring.

Lymphatic and blood vascular endothelial cells (ECs) share several molecular and developmental features. However, these two cell types possess distinct phenotypic signatures, reflecting their different biological functions. Despite significant advances in elucidating how the specification of lymphatic and blood vascular ECs is regulated at the transcriptional level during development, the key molecular mechanisms governing their lineage identity under physiological or pathological conditions remain poorly understood. To explore the epigenomic signatures in the maintenance of EC lineage specificity, we compared the transcriptomic landscapes, histone composition (H3K4me3 and H3K27me3) and DNA methylomes of cultured matched human primary dermal lymphatic and blood vascular ECs. Our findings reveal that blood vascular lineage genes manifest a more ‘repressed’ histone composition in lymphatic ECs, whereas DNA methylation at promoters is less linked to the differential transcriptomes of lymphatic versus blood vascular ECs. Meta-analyses identified two transcriptional regulators, BCL6 and MEF2C, which potentially govern endothelial lineage specificity. Notably, the blood vascular endothelial lineage markers CD34, ESAM and FLT1 and the lymphatic endothelial lineage markers PROX1, PDPN and FLT4 exhibited highly differential epigenetic profiles and responded in distinct manners to epigenetic drug treatments. The perturbation of histone and DNA methylation selectively promoted the expression of blood vascular endothelial markers in lymphatic endothelial cells, but not vice versa. Overall, our study reveals that the fine regulation of lymphatic and blood vascular endothelial transcriptomes is maintained via several epigenetic mechanisms, which are crucial to the maintenance of endothelial cell identity. Electronic supplementary material The online version of this article (10.1007/s10456-020-09743-9) contains supplementary material, which is available to authorized users.

In the last 15 years, single-cell technologies have become robust and indispensable tools to investigate cell heterogeneity. Beyond transcriptomic, genomic and epigenome analyses, technologies are constantly evolving, in particular toward multi-omics, where analyses of different source materials from a single cell are combined, and spatial transcriptomics, where resolution of cellular heterogeneity can be detected in situ. While some of these techniques are still being optimized, single-cell RNAseq has commonly been used because the examination of transcriptomes allows characterization of cell identity and, therefore, unravel previously uncharacterized diversity within cell populations. Most endocrine organs have now been investigated using this technique, and this has given new insights into organ embryonic development, characterization of rare cell types, and disease mechanisms. Here, we highlight recent studies, particularly on the hypothalamus and pituitary, and examine recent findings on the pancreas and reproductive organs where many single-cell experiments have been performed.

Summary Current technologies for single-cell transcriptomics allow thousands of cells to be analyzed in a single experiment. The increased scale of these methods raises the risk of cell doublets contamination. Available tools and algorithms for identifying doublets and estimating their occurrence in single-cell experimental data focus on doublets of different species, cell types or individuals. In this study, we analyze transcriptomic data from single cells having an identical genetic background. We claim that the ratio of monoallelic to biallelic expression provides a discriminating power toward doublets’ identification. We present a pipeline called BIallelic Ratio for Doublets (BIRD) that relies on heterologous genetic variations, from single-cell RNA sequencing. For each dataset, doublets were artificially created from the actual data and used to train a predictive model. BIRD was applied on Smart-seq data from 163 primary fibroblast single cells. The model achieved 100% accuracy in annotating the randomly simulated doublets. Bonafide doublets were verified based on a biallelic expression signal amongst X-chromosome of female fibroblasts. Data from 10X Genomics microfluidics of human peripheral blood cells achieved in average 83% (±3.7%) accuracy, and an area under the curve of 0.88 (±0.04) for a collection of ∼13 300 single cells. BIRD addresses instances of doublets, which were formed from cell mixtures of identical genetic background and cell identity. Maximal performance is achieved for high-coverage data from Smart-seq. Success in identifying doublets is data specific which varies according to the experimental methodology, genomic diversity between haplotypes, sequence coverage and depth. Supplementary information Supplementary data are available at Bioinformatics online.

A central goal in biology is to determine the ways in which evolution repeats itself. One of the most remarkable examples in nature of convergent evolutionary novelty is animal venom. Across diverse animal phyla, various specialized organs and anatomical structures have evolved from disparate developmental tissues to perform the same function, that is, produce and deliver a cocktail of potent molecules to subdue prey or predators. Venomous organisms therefore offer unique opportunities to investigate the evolutionary processes of convergence of key adaptive traits, and the molecular mechanisms underlying the emergence of novel genes, cells, and tissues. Indeed, some venomous species have already proven to be highly amenable as models for developmental studies, and recent work with venom gland organoids provides manipulatable systems for directly testing important evolutionary questions. Here, we provide a synthesis of the current knowledge that could serve as a starting point for the establishment of venom systems as new models for evolutionary and molecular biology. In particular, we highlight the potential of various venomous species for the study of cell differentiation and cell identity, and the regulatory dynamics of rapidly evolving, highly expressed, tissue-specific, gene paralogs. We hope that this review will encourage researchers to look beyond traditional study organisms and consider venom systems as useful tools to explore evolutionary novelties.

The cell’s shape and motion represent fundamental aspects of the cell identity, and can be highly predictive of the function and pathology. However, automated analysis of the morphodynamic states remains challenging for most cell types, especially primary human cells where genetic labeling may not be feasible. To enable automated and quantitative analysis of morphodynamic states, we developed DynaMorph – a computational framework that combines quantitative live cell imaging with self-supervised learning. To demonstrate the fidelity and robustness of this approach, we used DynaMorph to annotate morphodynamic states observed with label-free measurements of density and anisotropy of live microglia isolated from human brain tissue. These cells show complex behavior and have varied responses to disease-relevant stimuli. DynaMorph generates quantitative morphodynamic representations that can be used to evaluate the effects of disease-relevant perturbations. Using DynaMorph, we identify distinct morphodynamic states of microglia polarization and detect rare transition events between states. The methodologies presented here can facilitate automated discovery of functional states of diverse cellular systems.

The advent of induced pluripotent stem cell (iPSC) technology, which allows to transform one cell type into another, holds the promise to produce therapeutic cells and organs on demand. Realization of this objective is contingent on the ability to demonstrate quality and safety of the cellular product for its intended use. Bottlenecks and backlogs to the clinical use of iPSCs have been fully outlined and a need has emerged for safer and standardized protocols to trigger cell reprogramming and functional differentiation. Amidst great challenges, in particular associated with lengthy culture time and laborious cell characterization, a demand for faster and more accurate methods for the validation of cell identity and function at different stages of the iPSC manufacturing process has risen. Artificial intelligence-based methods are proving helpful for these complex tasks and might revolutionize the way iPSCs are managed to create surrogate cells and organs. Here, we briefly review recent progress in artificial intelligence approaches for evaluation of iPSCs and their derivatives in experimental studies. Graphical Abstract

CD8+ T cells not only are critical mediators of adaptive immunity but also may exhibit innate-like properties such as surface expression of NKG2C, an activating receptor typically associated with n...

The main challenge of single-cell RNA sequencing (scRNA-seq) studies arises from the large data sizes and various technical noises such as the excess zero counts within individual cells (called dropout events). This inaccurate measurement of gene expressions may introduce bias in downstream analyses of scRNA-seq data, so it is necessary to correct the false zero expression by computational imputation methods. Most current imputation pipelines typically use unsupervised modeling approaches that expect to recover biologically meaningful gene expression without known cell labels. However, unsupervised imputation often yields only limited gains since very little cell identity information is retained in the observed expression data. And for datasets with known cell labels, the use of cell label-guided imputation is expected to recover more accurate gene expression dynamics in different cell populations. In this work, we developed a supervised deep generative imputation model called scIDG to recover the biologically meaningful gene expression in scRNA-seq data with known cell type information. scIDG can learn both intrinsic features of the observed gene expression data and the interpretable representations of cell types, generating biologically meaningful imputation values under specified cell types and recovering gene expression dynamics. We tested scIDG on simulated and real scRNA-seq datasets and compared it with several state-of-the-art methods for imputation using cell type information. Experiments showed that scIDG can more accurately recover the heterogeneity of different cell types, significantly improving downstream differential expression analysis and temporal trajectory inference analysis.

During development, cell identity is established reproducibly among individuals through the expression of specific genes at the correct time and correct location in space. How genes extract and combine both positional and temporal information from different transcription factor (TF) profiles along polarity axes remain largely unexplored. Here, we showcase the classic hunchback gene in fruit fly embryos, with focus on 3 of its main TFs: Bicoid, Zelda and Hunchback proteins. We constructed a series of synthetic MS2 reporters, where the numbers and combination of binding sites for each TF are varied. Using live imaging of transcription dynamics by these synthetic reporters and modeling tools, we show that i) a Bicoid-only synthetic reporter needs 3 more Bicoid binding sites than found in the hunchback promoter to recapitulates almost all spatial features of early hb expression but takes more time to reach steady state; ii) Hunchback and Zelda binding sites combined with Bicoid sites both reduce the time to reach steady state and increase expression at a different step in the activation process: Zld sites lower the Bicoid threshold required for activation while Hb sites increase the polymerase firing rate and reduce bursting; iii) the shift of the Bicoid-only reporter induced by a reduction by half of Bicoid concentrations indicates that the decay length of the Bicoid activity gradient is lower than the decay length of the Bicoid protein gradient. Altogether, this work indicates that Bicoid is the main source of positional information for hunchback expression and places back the Bicoid system within the physical limits of an equilibrium model.

Introduction: The Renin-Angiotensin-System is essential to maintain blood pressure and fluid electrolyte homeostasis. Because precise regulation of expression and release of renin is critical for survival, understanding the molecular regulation of the renin cell identity is a vital area of study. Advances in epigenetics have enabled finer dissection of chromatin factors which maintain the identity of the renin cell. By studying genes with heightened accessibility profiles that are unique to the JG cell, we now have the capacity to unravel the determinants of the renin cell identity. Hypothesis: That transcription factors central to the governance of renin cell identity can be identified through the Assay for Transposase Accessible Chromatin (ATAC-seq) differential accessibility analysis. Methods: Native renin cell ATAC-seq was compared to existing ENCODE ATAC-seq datasets from 40 other cell types to define regions/peaks which characterize the JG program. Peaks with high intensity and ≥2-fold increase in signal were selected for Motif analysis to search for transcription factors (TFs) whose consensus sequence is enriched in those regions. Identified TFs were then selected for validation by in-situ hybridization and conditional deletion in renin cells. Results: 1) The Mef2c transcription factor was identified as having a consensus sequence in regulatory regions unique to the JG cell. It has clear expression in RNA-seq of renin cells (65 transcripts per million, n=3) and a predicted binding site in the renin gene. These results were validated by in-situ hybridization where signal localized at the JG area was detected in concordance with our in-silico results. 2) We generated Mef2c conditional knockout animals using our Ren1d-Cre mouse to study the effect in renin expression and identity. These mice displayed reduced renin immunostaining at the JG area and a 40% reduction in renin mRNA expression by qPCR from kidney cortices relative to wild-type (n=2, preliminary data). Conclusions: Our studies identified Mef2c as a TF target which likely has an essential role in maintaining and preserving renin cell identity. Experiments involving transcriptomics and epigenomics are ongoing to understand the changes wrought by Mef2c deletion in renin cells.

Summary Understanding the mechanisms that establish regulatory T (Treg) cell identity is central to understanding Treg cell function. van der Veeken et al. now show that the lineage-determining transcription factor Foxp3 establishes Treg-cell-specific chromatin architecture indirectly, mostly by decreasing the expression of other transcriptional regulators, including TCF1.

The decision of a blastomere to become inner cell mass or trophectoderm relies on the integration of two types of information: position and polarity. Compaction and polarization play key roles in this first lineage decision, since they alter both the intra- and the intercellular organization of the embryo. In-depth studies of early embryogenesis using the mouse model have provided new insights into the molecular regulation of compaction, polarization and lineage specification. However, how these processes first emerge and influence subsequent molecular and cellular events remain open questions in the field. In this review, we summarize the chain of events that lead to the generation of the first two cell lineages, outlining how compaction and polarization can build cell identity. Such processes, despite running in parallel, are subjected to different regulatory pathways. Then, if under specific circumstances one of the regulatory pathways is affected, embryos may achieve compaction but may have severe problems to acquire full developmental potential.

Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling—RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally defined neuron types can be classified by expression profile alone. The results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post hoc grouping, so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between 2 or more cell types. Therefore, this study supports the general utility of cell identification by transcriptional profiling, but adds a caution: It is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology, or innervation target can neuronal identity be unambiguously determined.

Several decades of intense research provided us with a grand framework describing the emergence of neurons in central (CNS) and peripheral (PNS) nervous systems. However, the specifics of molecular events and lineage control leading to a plethora of neuronal subtypes stayed largely unclear. Today, the advances in single cell omics, sample clearing and 3D-microscopy techniques, brain organoids, and synaptic connectivity tracing enabled systematic and unbiased understanding of neuronal diversity, development, circuitry and cell identity control. Novel technological advancements stimulated the transition from conceptual scheme of neuronal differentiation into precise maps of molecular events leading to the diversity of specific neuronal subtypes in relation to their locations and microenvironment. These high-resolution data opened a set of new questions including how transcriptional and epigenetics states control the proportionality of neuronal subpopulations or what are the evolutionary mechanisms of origin of different neuronal subtypes. In this review, we outline the most recent advancements in our understanding of how the neuronal diversity is generated in CNS and PNS and briefly address the challenges and questions arising in the field of neurogenesis.

To date, most attention on tissue regeneration has focused on the exploration of positive cues promoting or allowing the engagement of natural cellular restoration upon injury. In contrast, the signals fostering cell identity maintenance in the vertebrate body have been poorly investigated; yet they are crucial, for their counteraction could become a powerful method to induce and modulate regeneration. Here we review the mechanisms inhibiting pro-regenerative spontaneous adaptive cell responses in different model organisms and organs. The pharmacological or genetic/epigenetic modulation of such regenerative brakes could release a dormant but innate adaptive competence of certain cell types and therefore boost tissue regeneration in different situations.

Type 2 diabetes (T2D) is a widespread disease affecting millions in every continental population. Pancreatic β-cells are central to the regulation of circulating glucose, but failure in the maintenance of their mass and/or functional identity leads to T2D. Long non-coding RNAs (lncRNAs) represent a relatively understudied class of transcripts which growing evidence implicates in diabetes pathogenesis. T2D-associated single nucleotide polymorphisms (SNPs) have been identified in lncRNA loci, although these appear to function primarily through regulating β-cell proliferation. In the last decade, over 1100 lncRNAs have been catalogued in islets and the roles of a few have been further investigated, definitively linking them to β-cell function. These studies show that lncRNAs can be developmentally regulated and show highly tissue-specific expression. lncRNAs regulate neighbouring β-cell-specific transcription factor expression, with knockdown or overexpression of lncRNAs impacting a network of other key genes and pathways. Finally, gene expression analysis in studies of diabetic models have uncovered a number of lncRNAs with roles in β-cell function. A deeper understanding of these lncRNA roles in maintaining β-cell identity, and its deterioration, is required to fully appreciate the β-cell molecular network and to advance novel diabetes treatments.

International audience; While major advances in single cell transcriptomics have occurred, the epigenomics arena has only more recently gained momentum. Indeed, methods to track changes in different molecules present in biological samples have developed both in scale – providing a global view in each case – and in terms of resolution, allowing us today to disentangle heterogeneity within samples and access at the level of a single cell. The application of omics methods, comprising analysis of genomes, epigenomes, transcriptomes, proteomes and metabolomes, among other functional genomics techniques, individually or in combination, provides a wealth of valuable datasets. The treatment of these data with the development of artificial intelligence tools opens unprecedented avenues. The possibility to gain a holistic view of cells functioning within organs and during their formation will allow us to tackle fundamental questions of normal organism development and also to reveal early changes associated with disease onset and progression. In this editorial, we discuss the place of epigenomics research in an era where mapping cellular trajectories and mechanisms offers unique opportunities to address fundamental questions related to cell identity of key importance for medical applications.

Determining cell identity in volumetric images of tagged neuronal nuclei is an ongoing challenge in contemporary neuroscience. Frequently, cell identity is determined by aligning and matching tags to an “atlas” of labeled neuronal positions and other identifying characteristics. Previous analyses of suchC. elegansdatasets have been hampered by the limited accuracy of such atlases, especially for neurons present in the ventral nerve cord, and also by time-consuming manual elements of the alignment process. We present a novel automated alignment method for sparse and incomplete point clouds of the sort resulting from typicalC. elegansfluorescence microscopy datasets. This method involves a tunable learning parameter and a kernel that enforces biologically realistic deformation. We also present a pipeline for creating alignment atlases from datasets of the recently developed NeuroPAL transgene. In combination, these advances allow us to label neurons in volumetric images with confidence much higher than previous methods. We release, to the best of our knowledge, the most completeC. elegans3D positional neuron atlas, encapsulating positional variability derived from 7 animals, for the purposes of cell-type identity prediction for myriad applications (e.g., imaging neuronal activity, gene expression, and cell-fate).

Transient induction of pluripotent reprogramming factors has been reported to reverse some features of aging in mammalian cells and tissues. However, the impact of transient reprogramming on somatic cell identity programs and the necessity of individual pluripotency factors remain unknown. Here, we mapped trajectories of transient reprogramming in young and aged cells from multiple murine cell types using single cell transcriptomics to address these questions. We found that transient reprogramming restored youthful gene expression in adipocytes and mesenchymal stem cells but also temporarily suppressed somatic cell identity programs. We further screened Yamanaka Factor subsets and found that many combinations had an impact on aging gene expression and suppressed somatic identity, but that these effects were not tightly entangled. We also found that a transient reprogramming approach inspired by amphibian regeneration restored youthful gene expression in aged myogenic cells. Our results suggest that transient pluripotent reprogramming poses a neoplastic risk, but that restoration of youthful gene expression can be achieved with alternative strategies.

16 páginas, 6 figuras, 2 tablas, To systematically investigate the complexity of neuron specification regulatory networks, we performed an RNA interference (RNAi) screen against all 875 transcription factors (TFs) encoded in Caenorhabditis elegans genome and searched for defects in nine different neuron types of the monoaminergic (MA) superclass and two cholinergic motoneurons. We identified 91 TF candidates to be required for correct generation of these neuron types, of which 28 were confirmed by mutant analysis. We found that correct reporter expression in each individual neuron type requires at least nine different TFs. Individual neuron types do not usually share TFs involved in their specification but share a common pattern of TFs belonging to the five most common TF families: homeodomain (HD), basic helix loop helix (bHLH), zinc finger (ZF), basic leucine zipper domain (bZIP), and nuclear hormone receptors (NHR). HD TF members are overrepresented, supporting a key role for this family in the establishment of neuronal identities. These five TF families are also prevalent when considering mutant alleles with previously reported neuronal phenotypes in C. elegans, Drosophila, and mouse. In addition, we studied terminal differentiation complexity focusing on the dopaminergic terminal regulatory program. We found two HD TFs (UNC-62 and VAB-3) that work together with known dopaminergic terminal selectors (AST-1, CEH-43, CEH-20). Combined TF binding sites for these five TFs constitute a cis-regulatory signature enriched in the regulatory regions of dopaminergic effector genes. Our results provide new insights on neuron-type regulatory programs in C. elegans that could help better understand neuron specification and evolution of neuron types., the Bioinformatics and Biostatistics Unit from Principe Felipe Research Center (CIPF) for providing access to the cluster, cofunded by European Regional Development Funds (FEDER); Funding sources: European Research Council: ERC-StG2011-281920; ERC-Co-2020-101002203; Ministerio de Economía, Industria y Competitividad, Gobierno de España: SAF2017-84790-R; PID2020-115635RB-I00; RED2018-102553-T; Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana: PROMETEO/2018/055; ACIF/2019/079.

Since its discovery by F.A.L Clowes, extensive research has been dedicated to identifying the functions of the quiescent center (QC). One of the earliest hypotheses was that it serves a key role in regeneration of the root meristem. Recent works provided support for this hypothesis and began to elucidate the molecular mechanisms underlying this phenomenon. There are two scenarios to consider when assessing the role of the QC in regeneration: one, when the damage leaves the QC intact; and the other, when the QC itself is destroyed. In the first scenario, multiple factors are recruited to activate QC cell division in order to replace damaged cells, but whether the QC has a role in the second scenario is less clear. Both using gene expression studies and following the cell division pattern have shown that the QC is assembled gradually, only to appear as a coherent identity late in regeneration. Similar late emergence of the QC was observed during the de novo formation of the lateral root meristem. These observations can lead to the conclusion that the QC has no role in regeneration. However, activities normally occurring in QC cells, such as local auxin biosynthesis, are still found during regeneration but occur in different cells in the regenerating meristem. Thus, we explore an alternative hypothesis, that following destruction of the QC, QC-related gene activity is temporarily distributed to other cells in the regenerating meristem, and only coalesce into a distinct cell identity when regeneration is complete.

A hallmark of aging is loss of differentiated cell identity. AgedDrosophilamidgut differentiated enterocytes (ECs) lose their identity, impairing tissue homeostasis. To discover identity regulators, we performed an RNAi screen targeting ubiquitin-related genes in ECs. Seventeen genes were identified, including the deubiquitinase Non-stop (CG4166). Lineage tracing established that acute loss of Non-stop in young ECs phenocopies aged ECs at cellular and tissue levels. Proteomic analysis unveiled that Non-stop maintains identity as part of a Non-stop identity complex (NIC) containing E(y)2, Sgf11, Cp190, (Mod) mdg4, and Nup98. Non-stop ensured chromatin accessibility, maintaining the EC-gene signature, and protected NIC subunit stability. Upon aging, the levels of Non-stop and NIC subunits declined, distorting the unique organization of the EC nucleus. Maintaining youthful levels of Non-stop in wildtype aged ECs safeguards NIC subunits, nuclear organization, and suppressed aging phenotypes. Thus, Non-stop and NIC, supervise EC identity and protects from premature aging.

Diabetes is a group of metabolic disorders, which results from insufficient functional pancreatic β-cell mass either due to the autoimmune destruction of insulin producing β-cells, or their death or de-differentiation as compensation for insulin resistance. The ability to reprogram cell types within close developmental proximity to β-cells offers a strategy to replenish β-cell mass and a future possible treatment of diabetes. Here, we review recent advances in the fields of pancreas development and lineage reprogramming. We also probe the possibility of using reprogrammed cells as an approach by which to further understand developmental mechanisms, in particular roadblocks to changing cell identity. Finally, we highlight fundamental challenges that need to be overcome to advance lineage reprogramming for generating pancreatic cells.

The past decade revealed that cell identity changes, such as dedifferentiation or transdifferentiation, accompany the insulin-producing β-cell decay in most diabetes conditions. Mapping and controlling the mechanisms governing these processes is, thus, extremely valuable for managing the disease progression. Extracellular glucose is known to influence cell identity by impacting the redox balance. Here, we use global proteomics and pathway analysis to map the response of differentiating human pancreatic progenitors to chronically increased in vitro glucose levels. We show that exogenous high glucose levels impact different protein subsets in a concentration-dependent manner. In contrast, regardless of concentration, glucose elicits an antipodal effect on the proteome landscape, inducing both beneficial and detrimental changes in regard to achieving the desired islet cell fingerprint. Furthermore, we identified that only a subgroup of these effects and pathways are regulated by changes in redox balance. Our study highlights a complex effect of exogenous glucose on differentiating pancreas progenitors characterized by a distinct proteome signature. publishedVersion

Chromatin organization is essential to maintain a correct regulation of gene expression and establish cell identity. However, during cell division, the replication of the genetic material produces a global disorganization of chromatin structure. In this paper, we describe the new scientific breakthroughs that have revealed the nature of the post-replicative chromatin and the mechanisms that facilitate its restoration. Moreover, we highlight the implications of these chromatin alterations in gene expression control and their impact on key biological processes, such as cell differentiation, cell reprogramming or human diseases linked to cell proliferation, such as cancer., This work is part of the project PID2019-105742GA-100 funded by the MCIN/AEI/10.13039/ 501100011033. Research in the laboratory of C.G.-A. was also funded by the VI PPIT from University of Sevilla. C.G.-A. was recipient of a Ramón y Cajal contract (RYC2018-025485-I) funded by MCIN/AEI/10.13039/501100011033 and FSE “El FSE invierte en tu futuro”.

Cohesin is a ring-shaped protein complex that controls dynamic chromosome structure. Cohesin activity is important for a variety of biological processes, including formation of DNA loops that regulate gene expression. The precise mechanisms by which cohesin shapes local chromosome structure and gene expression are not fully understood. Recurrent mutations in cohesin complex members have been reported in various cancers, though it is not clear whether many cohesin sequence variants have phenotypes and contribute to disease. Here, we utilized CRISPR/Cas9 genome editing to introduce a variety of cohesin sequence variants into murine embryonic stem cells and investigate their molecular and cellular consequences. Some of the cohesin variants tested caused changes to transcription, including altered expression of gene encoding lineage-specifying developmental regulators. Altered gene expression was also observed at insulated neighborhoods, where cohesin-mediated DNA loops constrain potential interactions between genes and enhancers. Furthermore, some cohesin variants altered the proliferation rate and differentiation potential of murine embryonic stem cells. This study provides a functional comparison of cohesin variants found in cancer within an isogenic system, revealing the relative roles of various cohesin perturbations on gene expression and maintenance of cellular identity.

We present Cepo, a method to generate cell-type-specific gene statistics of differentially stable genes from single-cell RNA-sequencing (scRNA-seq) data to define cell identity. Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells, and lineage inference of single cells.

Protoplasts are a versatile and powerful cell-based system to study different plant processes in vivo, due to their ability to maintain cell identity and carry out reactions and metabolic processes similar to intact plants. In rice, despite numerous reports, difficulties are encountered in protoplast isolation and transfection. These include insufficient numbers of protoplasts isolated and inefficient transfection. Such difficulties limit the use of this simple yet useful technology. The need to use protoplasts is particularly important when similar experiments may not work in yeast or Pichia, due to differences in functionally essential protein post-translation modifications. In this chapter, we describe a rice protoplast isolation and transfection method.

Direct-geneFISH is a Fluorescence In Situ Hybridization (FISH) method that directly links gene presence, and thus potential metabolic capabilities, to cell identity. The method uses rRNA-targeting oligonucleotide probes to identify cells and dsDNA polynucleotide probes carrying multiple molecules of the same fluorochrome to detect genes. In addition, direct-geneFISH allows quantification of the cell fraction carrying the targeted gene and the number of target genes per cell. It can be applied to laboratory cultures, for example, enrichments and phage infections, and to environmental samples. This book chapter describes the main steps of the direct-geneFISH protocol: probe design and synthesis, the "core" direct-geneFISH protocol and lastly, microscopy and data analysis.

Objective. In tetrode recordings, the cell types of the recorded units are difficult to determine based on electrophysiological characteristics alone. Optotagging, the use of optogenetic stimulation to precisely identify cells, is a method to overcome this challenge. However, recording from many different cells requires advancing electrodes and light sources slowly through the brain with a microdrive. Existing designs suffer from a number of drawbacks, such as limited stability and precision, high cost, complex assembly, or excessive size and weight. Approach. We designed TetrODrive as a microdrive that can be 3D printed on an inexpensive desktop resin printer, has minimal parts, assembly time, and cost. The microdrive can be assembled in 15 min and the price for all materials, including the 3D printer, is lower than a single commercial microdrive. To maximize recording stability, we mechanically decoupled the drive mechanism from the electrical and optical connectors. Main results. The developed microdrive is small and light enough (Significance. TetrODrive is a tiny, lightweight, and affordable microdrive for optophysiology in mice. Its open design, price, and built-in characteristics can significantly expand the use of microdrives in mice.

Bidirectional cell-cell communication via paracrine mechanisms involving nano-sized extracellular vesicles have emerged as a predominant mechanism of cellular signaling. Unlike other shedding vesicles of similar size, exosomes selectively package their cargo using defined mechanisms within the cells. Recent research on exosome signaling describes a messenger-recipient cell dichotomy. The heterogeneous origin of exosome populations, although previously described, has as-yet been incompletely characterized using this dichotomy and thus does not currently provide a complete understanding of exosome populations. In this work, we outline the fundamentally bidirectional nature of exosomes and replace this dichotomy with a messenger-recipient-effector network formed by repackaging and rerelease events. This network further confounds the determination of messenger cell identity among an already heterogeneous exosome population and has major implications for future clinical application. Redefining the axiom of exosome signaling provides a route for future research to consider a multi-system-based approach and underscores a need for enhanced identification methods. This shift also has implications for the use of exosomes as therapeutic agents. Exosome biogenesis and its manipulation will be crucial for the development of curative endogenous exosomes and their synthetic, exogenously produced counterparts. Directed cargo loading, optimal shell composition, and robust production platforms are just some of the design aspects that need to be considered. As tissue-specific therapeutic agents, exosome design will also need to incorporate repackaging mechanics to prevent off-target effects and increase efficacy. A comprehensive current understanding of exosome biogenesis mechanisms amidst the heterogenous EV population will propel the field towards clinical viability.

Motivation Hematopoietic stem cells (HSCs) give rise to all blood cells and play a vital role throughout the whole lifespan through their pluripotency and self-renewal properties. Accurately identifying the stages of early HSCs is extremely important, as it may open up new prospects for extracorporeal blood research. Existing experimental techniques for identifying the early stages of HSCs development are time-consuming and expensive. Machine learning has shown its excellence in massive single-cell data processing and it is desirable to develop related computational models as good complements to experimental techniques. Results In this study, we presented a novel predictor called eHSCPr specifically for predicting the early stages of HSCs development. To reveal the distinct genes at each developmental stage of HSCs, we compared F-score with three state-of-art differential gene selection methods (limma, DESeq2, edgeR) and evaluated their performance. F-score captured the more critical surface markers of endothelial cells and hematopoietic cells, and the area under receiver operating characteristic curve (ROC) value was 0.987. Based on SVM, the 10-fold cross-validation accuracy of eHSCpr in the independent dataset and the training dataset reached 94.84% and 94.19%, respectively. Importantly, we performed transcription analysis on the F-score gene set, which indeed further enriched the signal markers of HSCs development stages. eHSCPr can be a powerful tool for predicting early stages of HSCs development, facilitating hypothesis-driven experimental design and providing crucial clues for the in vitro blood regeneration studies. Availability and implementation http://bioinfor.imu.edu.cn/ehscpr. Supplementary information Supplementary data are available at Bioinformatics online.

Spatial transcriptomics technologies promise to reveal spatial relationships of cell-type composition in complex tissues. However, the development of computational methods that can utilize the unique properties of spatial transcriptome data to unveil cell identities remains a challenge. Here, we introduce SpiceMix, a new interpretable method based on probabilistic, latent variable modeling for effective joint analysis of spatial information and gene expression from spatial transcriptome data. Both simulation and real data evaluations demonstrate that SpiceMix markedly improves upon the inference of cell types and their spatial patterns compared with existing approaches. By applying to spatial transcriptome data of brain regions in human and mouse acquired by seqFISH+, STARmap, and Visium, we show that SpiceMix can enhance the inference of complex cell identities, reveal interpretable spatial metagenes, and uncover differentiation trajectories. SpiceMix is a generalizable framework for analyzing spatial transcriptome data to provide critical insights into the cell type composition and spatial organization of cells in complex tissues.

In October 2020, the Keystone Symposia Global Health Series hosted a Keystone eSymposia entitled ‘Tissue Plasticity: Preservation and Alteration of Cellular Identity’. The event synthesized groundbreaking research from unusually diverse fields of study, presented in various formats, including live and virtual talks, panel discussions and interactive e-poster sessions. The meeting focused on cell identity changes and plasticity in multiple tissues, species and developmental contexts, both in homeostasis and during injury. Here, we review the key themes of the meeting: (1) cell-extrinsic drivers of plasticity; (2) epigenomic regulation of cell plasticity; and (3) conserved mechanisms governing plasticity. A salient take-home conclusion was that there may be conserved mechanisms used by cells to execute plasticity, with autodegradative activity (autophagy and lysosomes) playing a crucial initial step in diverse organs and organisms.

During development, neurons extend in search of synaptic partners. Precise control of axon extension velocity can therefore be crucial to ensuring proper circuit formation. How velocity is regulated – particularly by the extending axons themselves – remains poorly understood. Here, we investigate this question in theDrosophilavisual system, where photoreceptors make precise connections with a specific set of synaptic partners that together create a circuit underpinning neural superposition (NSP). We used a combination of genetic perturbations and quantitative image analysis to investigate the influence of cell identity on growth cone velocity and subsequent spatial-temporal coincidence of presynaptic and postsynaptic neurons. Our study provides a case study of how cell autonomous properties of presynaptic axons play a pivotal role in controlling the dynamics of growing axons and determining the formation of a precise neuronal circuit.

Hi-C experiments are a powerful means to describe the organization of chromatin interactions genome-wide. By using Hi-C data to identify differentially organized genomic regions, relationships between this organization, gene expression, and cell identity may be established. However, Hi-C data exhibit a unique and challenging spatial structure, as genomic loci can show strong correlations when they are nearby in 3D space within the nucleus or 1D space along the chromosome. Consequently, the development of methods that can accurately detect differences between Hi-C samples while controlling false discoveries has remained difficult. To meet this need, we introduce a spatial modeling approach based on sliding window statistics. Using polymer simulations, we illustrate the improved power and precision of our method to identify differentially interacting genomic regions. We further demonstrate our method’s ability to reveal biologically meaningful changes in chromatin architecture through two data analyses concerning the loss of architectural and chromatin remodeling proteins.

Integrated single-cell gene regulatory network from three mouse cell atlases captures global and cell type–specific regulatory modules and crosstalk, important for cellular identity., Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type–specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.

Protein cofactor Ldb1 regulates cell fate specification by interacting with LIM-homeodomain (LIM-HD) proteins in a tetrameric complex consisting of an LDB:LDB dimer that bridges two LIM-HD molecules, a mechanism first demonstrated in theDrosophilawing disc. Here, we demonstrate conservation of this interaction in the regulation of mammalian hippocampal development, which is profoundly defective upon loss of eitherLhx2orLdb1. Electroporation of a chimeric construct that encodes the Lhx2-HD and Ldb1-DD (dimerization domain) in a single transcript cell-autonomously rescues a comprehensive range of hippocampal deficits in the mouseLdb1mutant, including the acquisition of field-specific molecular identity and the regulation of the neuron-glia cell fate switch. This demonstrates that the LHX:LDB complex is an evolutionarily conserved molecular regulatory device that controls complex aspects of regional cell identity in the developing brain.Summary statementSimilar to an Apterous-Chip mechanism that patterns the Drosophila wing blade, interaction between mammalian orthologs Lhx2 and Ldb1 regulates multiple aspects of hippocampal development in the mouse.

While root diseases are among the most devastating stresses in global crop production, our understanding of root immunity is still limited relative to our knowledge of immune responses in leaves. Considering that root performance is based on the concerted functions of its different cell types, we undertook a cell type-specific transcriptome analysis to identify gene networks activated in epidermis, cortex, and pericycle cells of Arabidopsis (Arabidopsis thaliana) roots challenged with two immunity elicitors, the bacterial flagellin-derived flg22 and the endogenous Pep1 peptide. Our analyses revealed distinct immunity gene networks in each cell type. To further substantiate our understanding of regulatory patterns underlying these cell type-specific immunity networks, we developed a tool to analyze paired transcription factor binding motifs in the promoters of cell type-specific genes. Our study points toward a connection between cell identity and cell type-specific immunity networks that might guide cell types in launching immune response according to the functional capabilities of each cell type.

Annotating cell identities is a common bottleneck in the analysis of single-cell genomics experiments. Here, we present scNym, a semisupervised, adversarial neural network that learns to transfer cell identity annotations from one experiment to another. scNym takes advantage of information in both labeled data sets and new, unlabeled data sets to learn rich representations of cell identity that enable effective annotation transfer. We show that scNym effectively transfers annotations across experiments despite biological and technical differences, achieving performance superior to existing methods. We also show that scNym models can synthesize information from multiple training and target data sets to improve performance. We show that in addition to high accuracy, scNym models are well calibrated and interpretable with saliency methods.

The Encyclopedia of DNA Elements (ENCODE) project has established a genomic resource for mammalian development, profiling a diverse panel of mouse tissues at 8 developmental stages from 10.5 days after conception until birth, including transcriptomes, methylomes and chromatin states. Here we systematically examined the state and accessibility of chromatin in the developing mouse fetus. In total we performed 1,128 chromatin immunoprecipitation with sequencing (ChIP–seq) assays for histone modifications and 132 assay for transposase-accessible chromatin using sequencing (ATAC–seq) assays for chromatin accessibility across 72 distinct tissue-stages. We used integrative analysis to develop a unified set of chromatin state annotations, infer the identities of dynamic enhancers and key transcriptional regulators, and characterize the relationship between chromatin state and accessibility during developmental gene regulation. We also leveraged these data to link enhancers to putative target genes and demonstrate tissue-specific enrichments of sequence variants associated with disease in humans. The mouse ENCODE data sets provide a compendium of resources for biomedical researchers and achieve, to our knowledge, the most comprehensive view of chromatin dynamics during mammalian fetal development to date., Analysis of chromatin state and accessibility in mouse tissues from twelve sites and eight developmental stages provides a comprehensive view of chromatin dynamics.

The exhaustive exploration of human cell heterogeneity requires the unbiased identification of molecular signatures that can serve as unique cell identity cards for every cell in the body. However, the stochasticity associated with high-throughput single-cell sequencing has made it necessary to use clustering-based computational approaches in which the characterization of cell-type heterogeneity is performed at cell-subpopulation level rather than at full single-cell resolution. We present here Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. Cell-ID signatures allow unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics technologies. Cell-ID is distributed as an open-source R software package:https://github.com/RausellLab/CelliD.