High-throughput Single-cell RNA Research Articles

Construction and Validation of Prognostic Regulation Network Based on RNA-Binding Protein Genes in Lung Squamous Cell Carcinoma.

Lung squamous cell carcinoma (LUSC) is a common histologic subtype of non-small cell lung cancer with a poor prognosis. RNA-binding proteins (RBPs) are key modulators in the posttranscriptional regulation and RBP alterations are commonly found in various cancer types. However, its roles in predicting the tumorigenesis and prognosis have not been identified in LUSC. To identify the roles of RBPs in the tumorigenesis and prognosis of LUSC, the RNA sequencing data of patients with LUSC were retrieved from The Cancer Genome Atlas (TCGA) databases. The differential expressed genes (DEGs) were evaluated and identified. The intersection of manually curated RBPs and tumorigenesis-related DEGs was filtered to the univariate Cox regression analysis. The intersection genes with prognostic value were defined as prognostic RNA-binding protein genes (PRBPGs). Based on them, the predicted model was constructed. Its accuracy was tested by the area under the curve (AUC) of the receiver operator characteristic curve and the risk score. In addition, to explore the key regulatory network, the relationship among PRBPGs, target RNA, and absolute quantification of 50 hallmarks of cancer was also identified by Pearson correlation analysis. A total of 311 genes were filtered as the intersection of 1542 manually curated RBPs and tumorigenesis-related DEGs and the results revealed 17 PRBPGs. Based on them, we constructed the predict model with a relatively high accuracy (AUC: 0.739). The Kaplan-Meier survival curve showed the significant prognostic value of risk score (p < 0.001). Moreover, we uncovered the regulatory networks of PHF5A-TOMM22-oxidative phosphorylation, TLR3-CTSO inflammation-related pathway, SECISBP2L-targeted RNA (ADGRF5, TGFBR2, CD302, AC096921.2, AHCYL2, RPS6KA2, SLC34A2, and SFTPB) angiogenesis, and SECISBP2L-AKAP13 signaling (DNA repair, MTORC1 signaling, and MYC targets). The regulation mechanisms and cellular location of key PRBPGs were validated by assay for targeting accessible chromatin with high-throughput sequencing and single-cell RNA sequencing. Our study identifies PRBPGs as reliable indexes in predicting the tumorigenesis and prognosis of patients with LUSC and provides a well-applied model for predicting the overall survival for patients with LUSC. Besides, we also identified the regulatory network among PRBPGs, target RNA, and cancer gene sets in mediating the LUSC tumorigenesis.

Single-cell RNA sequencing reveals a high-resolution cell atlas of xylem in Populus.

High-throughput single-cell RNA sequencing (scRNA-seq) has advantages over traditional RNA-seq to explore spatiotemporal information on gene dynamic expressions in heterogenous tissues. We performed Drop-seq, a method for the dropwise sequestration of single cells for sequencing, on protoplasts from the differentiating xylem of Populus alba × Populus glandulosa. The scRNA-seq profiled 9,798 cells, which were grouped into 12 clusters. Through characterization of differentially expressed genes in each cluster and RNA in situ hybridizations, we identified vessel cells, fiber cells, ray parenchyma cells and xylem precursor cells. Diffusion pseudotime analyses revealed the differentiating trajectory of vessels, fiber cells and ray parenchyma cells and indicated a different differentiation process between vessels and fiber cells, and a similar differentiation process between fiber cells and ray parenchyma cells. We identified marker genes for each cell type (cluster) and key candidate regulators during developmental stages of xylem cell differentiation. Our study generates a high-resolution expression atlas of wood formation at the single cell level and provides valuable information on wood formation.

The Parkinson's-disease-associated mutation LRRK2-G2019S alters dopaminergic differentiation dynamics via NR2F1.

Increasing evidence suggests that neurodevelopmental alterations might contribute to increase the susceptibility to develop neurodegenerative diseases. We investigate the occurrence of developmental abnormalities in dopaminergic neurons in a model of Parkinson's disease (PD). We monitor the differentiation of human patient-specific neuroepithelial stem cells (NESCs) into dopaminergic neurons. Using high-throughput image analyses and single-cell RNA sequencing, we observe that the PD-associated LRRK2-G2019S mutation alters the initial phase of neuronal differentiation by accelerating cell-cycle exit with a concomitant increase in cell death. We identify the NESC-specific core regulatory circuit and a molecular mechanism underlying the observed phenotypes. The expression of NR2F1, a key transcription factor involved in neurogenesis, decreases in LRRK2-G2019S NESCs, neurons, and midbrain organoids compared to controls. We also observe accelerated dopaminergic differentiation invivo in NR2F1-deficient mouse embryos. This suggests a pathogenic mechanism involving the LRRK2-G2019S mutation, where the dynamics of dopaminergic differentiation are modified via NR2F1.

Discovering Myeloid Cell Heterogeneity in Mandibular Bone - Cell by Cell Analysis.

The myeloid-derived bone marrow progenitor populations from different anatomical locations are known to have diverse osteoclastogenesis potential. Specifically, myeloid progenitors from the tibia and femur have increased osteoclast differentiation potential compared to myeloid progenitors from the alveolar process. In this study, we explored the differences in the myeloid lineage progenitor cell populations in alveolar (mandibular) bone versus long (femur) bone using flow cytometry and high-throughput single cell RNA sequencing (scRNA-seq) to provide a comprehensive transcriptional landscape. Results indicate that mandibular bone marrow-derived cells exhibit consistent deficits in myeloid differentiation, including significantly fewer myeloid-derived suppressor cell (MDSC)-like populations (CD11b+Ly6C+, CD11b+Ly6G+), as well as macrophages (CD11b+F4/80+). Although significantly fewer in number, MDSCs from mandibular bone exhibited increased immunosuppressive activity compared to MDSCs isolated from long bone. Using flow cytometry panels specific for bone marrow progenitors, analysis of hematopoietic stem cells showed no defects in mandibular bone marrow in LSK (Lin–Sca1+cKit+) cell and LK (Lin–Sca1–cKit+) cell populations. While there was no significant difference in granulocyte progenitors, the granulocyte-monocyte progenitors and monocyte progenitor population were significantly decreased in the mandibular bone marrow. T-lymphocyte subsets were not significantly different between mandibular and femoral bone, except for CD4+CD25+Foxp3+ regulatory T lymphocytes, which were significantly increased in the mandible. In addition, B lymphocytes were significantly increased in mandible. Single cell RNA sequencing from mandible and femur BM revealed distinct differences in transcriptomic profiles in myeloid populations establishing previously unappreciated aspects of mandibular bone marrow populations. These analyses reveal site-specific differences in the myeloid progenitor cellular composition and transcriptional programs providing a deeper appreciation of the complex differences in myeloid cell heterogeneity from different anatomical bone marrow sites.

High-throughput RNA sequencing of paraformaldehyde-fixed single cells

Single-cell transcriptomic studies that require intracellular protein staining, rare cell sorting, or inactivation of infectious pathogens are severely limited. This is because current high-throughput single-cell RNA sequencing methods are either incompatible with or necessitate laborious sample preprocessing for paraformaldehyde treatment, a common tissue and cell fixation and preservation technique. Here we present FD-seq (Fixed Droplet RNA sequencing), a high-throughput method for droplet-based RNA sequencing of paraformaldehyde-fixed, permeabilized and sorted single cells. We show that FD-seq preserves the RNA integrity and relative gene expression levels after fixation and permeabilization. Furthermore, FD-seq can detect a higher number of genes and transcripts than methanol fixation. We first apply FD-seq to analyze a rare subpopulation of cells supporting lytic reactivation of the human tumor virus KSHV, and identify TMEM119 as a potential host factor that mediates viral reactivation. Second, we find that infection with the human betacoronavirus OC43 leads to upregulation of pro-inflammatory pathways in cells that are exposed to the virus but fail to express high levels of viral genes. FD-seq thus enables integrating phenotypic with transcriptomic information in rare cell subpopulations, and preserving and inactivating pathogenic samples.

Cell-specific gene association network construction from single-cell RNA sequence

ABSTRACT The recent development of a high throughput single-cell RNA sequence devises the opportunity to study entire transcriptomes in the smallest detail. It also leads to the characterization of molecules and subtypes of a cell. Cancer epigenetics induced not only from individual molecules but also from the dysfunction of the system and the coupling effect of genes. While rapid advances are being made in the development of tools for single-cell RNA-seq data analysis, few slants are noticed in the potential advantages of single-cell network construction. Here, we used network perturbation theory with significant analysis to develop a cell-specific network that provides an insight into gene–gene association based on molecular expressions in a single-cell resolution. Besides, using this method, we can characterize each cell by inspecting how genes are connected and can identify the hub genes using network degree theory. Pathway & Gene enrichment analysis of the identified cell-specific high network degree genes supported the effectiveness of this method. This method could be beneficial for personalized drug design and even therapeutics.

RPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database.

MotivationCharacterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization.ResultsHere, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes’ unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway.Availability and implementationrPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB.

Generating and Using Transcriptomically Based Retinal Cell Atlases.

It has been known for over a century that the basic organization of the retina is conserved across vertebrates. It has been equally clear that retinal cells can be classified into numerous types, but only recently have methods been devised to explore this diversity in unbiased, scalable, and comprehensive ways. Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have played a pivotal role in this effort. In this article, we outline the experimental and computational components of scRNA-seq and review studies that have used them to generate retinal atlases of cell types in several vertebrate species. These atlases have enabled studies of retinal development, responses of retinal cells to injury, expression patterns of genes implicated in retinal disease, and the evolution of cell types. Recently, the inquiry has expanded to include the entire eye and visual centers in the brain. These studies have enhanced our understanding of retinal function and dysfunction and provided tools and insights for exploring neural diversity throughout the brain.

Abstract 237: Inferring spatial organization of tumor microenvironment from single-cell RNA sequencing data using graph embedding

Abstract Spatial heterogeneity of diverse cellular components in the tumor microenvironment (TME) plays a critical role in the reprogramming of tumor initiation, growth, invasion, metastasis, and response to therapies. Systematic knowledge of TME spatial organization with regards to immune infiltration and tumor resource distribution is of high clinical significance. High throughput single-cell RNA sequencing (scRNA-seq) has become a revolutionary approach for studying cell composition and the development of TME. However, the spatial information of cells is lost as the tissue must be dissociated before the sequencing is performed. While various spatial techniques are emerging, their applicability is still rather limited. To address this challenge computationally, we develop a novel de novo framework to reconstruct TME spatial organization from scRNA-seq data. We hypothesized that cell spatial organization in a microenvironment is mainly determined by cell identity and interactions between different cells. In particular, the spatial organization of structural cells and immune cells follow different mechanisms. Neighboring structural cells, which share similar whole transcriptome profiles, form a scaffold of the TME; immune cells, whose activities are influenced by the structural cells, migrate in the scaffold to interact with structural cells and exert their functions. The algorithm models the scaffold of structural cells using adaptive nearest neighbor graph by taking the cell density estimation into the consideration, where the nearest neighbor graph was further augmented by inserting immune cells into the appropriate locations of the scaffold according to the LR similarities. To reconstruct 3D spatial organization while preserving the cell topology represented by the graph, we employed a graph embedding strategy to minimize the discrepancy between the graph topology and the embedded 3D space. We evaluated the framework on two diffuse intrinsic pontine gliomas (DIPG) samples from a mouse model with coupled scRNA-seq and spatial transcriptome (ST, 10x Visium platform) data. The predicted spatial organization successfully separates the major cell types. The T-cell infiltrated tumor, verified by the T-cell spatial spots of the ST image, is well recapitulated. We deconvoluted the ST data by integrating the scRNA-seq data using SPOTlight. The neighborhood enrichment distributions of predicted spatial organization and the spot deconvoluted ST data show high consistency as measured by Kullback-Leibler divergence. We found heterogeneous neighborhood composition of CD8+ T-cells, indicating diverse clonality and functions with respect to their locations in the TME. Citation Format: Liang Ding, Hao Shi, Koon-Kiu Yan, Yogesh Dhungana, Sivaraman Natarajan, John Easton, Jason Chiang, Christopher L. Tinkle, Xiaoyan Zhu, Liming Cai, Suzanne J. Baker, Hongbo Chi, Jiyang Yu. Inferring spatial organization of tumor microenvironment from single-cell RNA sequencing data using graph embedding [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 237.

A single-cell guide to retinal development: Cell fate decisions of multipotent retinal progenitors in scRNA-seq

Recent advances in high throughput single-cell RNA sequencing (scRNA-seq) technology have enabled the simultaneous transcriptomic profiling of thousands of individual cells in a single experiment. To investigate the intrinsic process of retinal development, researchers have leveraged this technology to quantify gene expression in retinal cells across development, in multiple species, and from numerous important models of human disease. In this review, we summarize recent applications of scRNA-seq and discuss how these datasets have complemented and advanced our understanding of retinal progenitor cell competence, cell fate specification, and differentiation. Finally, we also highlight the outstanding questions in the field that advances in single-cell data generation and analysis will soon be able to answer.

Spatio-temporal mRNA tracking in the early zebrafish embryo

Early stages of embryogenesis depend on subcellular localization and transport of maternal mRNA. However, systematic analysis of these processes is hindered by a lack of spatio-temporal information in single-cell RNA sequencing. Here, we combine spatially-resolved transcriptomics and single-cell RNA labeling to perform a spatio-temporal analysis of the transcriptome during early zebrafish development. We measure spatial localization of mRNA molecules within the one-cell stage embryo, which allows us to identify a class of mRNAs that are specifically localized at an extraembryonic position, the vegetal pole. Furthermore, we establish a method for high-throughput single-cell RNA labeling in early zebrafish embryos, which enables us to follow the fate of individual maternal transcripts until gastrulation. This approach reveals that many localized transcripts are specifically transported to the primordial germ cells. Finally, we acquire spatial transcriptomes of two xenopus species and compare evolutionary conservation of localized genes as well as enriched sequence motifs.

PlantscRNAdb: A database for plant single-cell RNA analysis

PlantscRNAdb: A database for plant single-cell RNA analysis

MiRNA activity inferred from single cell mRNA expression

High throughput single-cell RNA sequencing (scRNAseq) can provide mRNA expression profiles for thousands of cells. However, miRNAs cannot currently be studied at the same scale. By exploiting that miRNAs bind well-defined sequence motifs and typically down-regulate target genes, we show that motif enrichment analysis can be used to derive miRNA activity estimates from scRNAseq data. Motif enrichment analyses have traditionally been used to derive binding motifs for regulatory factors, such as miRNAs or transcription factors, that have an effect on gene expression. Here we reverse its use. By starting from the miRNA seed site, we derive a measure of activity for miRNAs in single cells. We first establish the approach on a comprehensive set of bulk TCGA cancer samples (n = 9679), with paired mRNA and miRNA expression profiles, where many miRNAs show a strong correlation with measured expression. By downsampling we show that the method can be used to estimate miRNA activity in sparse data comparable to scRNAseq experiments. We then analyze a human and a mouse scRNAseq data set, and show that for several miRNA candidates, including liver specific miR-122 and muscle specific miR-1 and miR-133a, we obtain activity measures supported by the literature. The methods are implemented and made available in the miReact software. Our results demonstrate that miRNA activities can be estimated at the single cell level. This allows insights into the dynamics of miRNA activity across a range of fields where scRNAseq is applied.

Single-Cell Transcriptomics Supports a Role of CHD8 in Autism.

Chromodomain helicase domain 8 (CHD8) is one of the most frequently mutated and most penetrant genes in the autism spectrum disorder (ASD). Individuals with CHD8 mutations show leading symptoms of autism, macrocephaly, and facial dysmorphisms. The molecular and cellular mechanisms underpinning the early onset and development of these symptoms are still poorly understood and prevent timely and more efficient therapies of patients. Progress in this area will require an understanding of “when, why and how cells deviate from their normal trajectories”. High-throughput single-cell RNA sequencing (sc-RNAseq) directly quantifies information-bearing RNA molecules that enact each cell’s biological identity. Here, we discuss recent insights from sc-RNAseq of CRISPR/Cas9-editing of Chd8/CHD8 during mouse neocorticogenesis and human cerebral organoids. Given that the deregulation of the balance between excitation and inhibition (E/I balance) in cortical and subcortical circuits is thought to represent a major etiopathogenetic mechanism in ASD, we focus on the question of whether, and to what degree, results from current sc-RNAseq studies support this hypothesis. Beyond that, we discuss the pros and cons of these approaches and further steps to be taken to harvest the full potential of these transformative techniques.

ScRNA-seq of ovarian follicle granulosa cells from different fertility goats reveals distinct expression patterns.

The new technology of high-throughput single-cell RNA sequencing (10×scRNA-seq) was developed recently with many advantages. However, it was not commonly used in farm animal research. There are few reports for the gene expression of goat ovarian follicle granulosa cells (GCs) during different developmental stages. In the current investigation, the gene expression of follicle GCs at different stages from two populations of Ji'ning grey goats: high litter size (HL; ≥3/L; 2L) and low litter size (LL; ≤2 /L; 2L) were analysed by scRNA-seq. Many GC marker genes were identified, and the pseudo-time showed that GCs developed during the time course which reflected the follicular development and differentiation trajectory. Moreover, the gene expression difference between the two populations HL versus LL was very clear at different developmental stages. Many marker genes differentially expressed at different developmental stages. ASIP and ASPN were found to be highly expressed in the early stage of GCs, INHA, INHBA, MFGE8 and HSD17B1 were identified to be highly expressed in the growing stage of GCs, while IGFBP2, IGFBP5 and CYP11A1 were found to be highly expressed in late stage. These marker genes could be used as reference genes of goat follicle GC development. This investigation for the first time discovered the gene expression patterns in goat follicle GCs in high- or low-fertility populations (based on litter size) by scRNA-seq which may be useful for uncovering the oocyte development potential.

Novel Molecular Hallmarks of Group 3 Medulloblastoma by Single-Cell Transcriptomics

Medulloblastoma (MB) is a highly heterogeneous and one of the most malignant pediatric brain tumors, comprising four subgroups: Sonic Hedgehog, Wingless, Group 3, and Group 4. Group 3 MB has the worst prognosis of all MBs. However, the molecular and cellular mechanisms driving the maintenance of malignancy are poorly understood. Here, we employed high-throughput single-cell and bulk RNA sequencing to identify novel molecular features of Group 3 MB, and found that a specific cell cluster displayed a highly malignant phenotype. Then, we identified the glutamate receptor metabotropic 8 (GRM8), and AP-1 complex subunit sigma-2 (AP1S2) genes as two critical markers of Group 3 MB, corresponding to its poor prognosis. Information on 33 clinical cases was further utilized for validation. Meanwhile, a global map of the molecular cascade downstream of the MYC oncogene in Group 3 MB was also delineated using single-cell RNA sequencing. Our data yields new insights into Group 3 MB molecular characteristics and provides novel therapeutic targets for this relentless disease.

A single-cell analysis of the Arabidopsis vegetative shoot apex.

The shoot apical meristem allows for reiterative formation of new aerial structures throughout the life cycle of a plant. We use single-cell RNA sequencing to define the cellular taxonomy of the Arabidopsis vegetative shoot apex at the transcriptome level. We find that the shoot apex is composed of highly heterogeneous cells, which can be partitioned into 7 broad populations with 23 transcriptionally distinct cell clusters. We delineate cell-cycle continuums and developmental trajectories of epidermal cells, vascular tissue, and leaf mesophyll cells and infer transcription factors and gene expression signatures associated with cell fate decisions. Integrative analysis of shoot and root apical cell populations further reveals common and distinct features of epidermal and vascular tissues. Our results, thus, offer a valuable resource for investigating the basic principles underlying cell division and differentiation in plants at single-cell resolution.

Complete Freund's adjuvant-induced decrement of pruriceptor-mediated suppression of itch.

Peripheral inflammation is always accompanied by a noxious sensation, either pain or itch, providing a protective warning for the occurrence of pathological changes; however, the mechanisms determining whether pain, itch, or both will be elicited under certain inflammatory statuses are still far from clear. Complete Freund's adjuvant (CFA) contains heat killed and dried Mycobacterium tuberculosis widely used to induce inflammatory pain models, but how CFA treatment affects itch sensation and the possible mechanisms are still unclear. In this study, using itch behavior testing and calcium imaging, we showed that both the behaviors and calcium responses associated with Transient Receptor Potential Vanilloid 1 (TRPV1)-mediated histamine-dependent itch and Transient Receptor Potential Ankyrin 1 (TRPA1)-mediated histamine-independent itch were significantly suppressed by CFA treatment. Furthermore, to explore the possible cellular mechanisms, high-throughput single-cell RNA sequencing and real-time PCR were used to detect CFA-induced changes of itch-related genes in dorsal root ganglion (DRG) neurons. Our results revealed that although both nociceptive Trpv1+ and Trpa1+ DRG neurons were increased after CFA treatment, most known pruriceptors, including Hrh1+, Mrgpra3+, Mrgprd+, Htr3a+, Htr1f+, IL31ra+, Osmr+, and Lpar3+ DRG neurons, were significantly decreased, which may explain that CFA treatment caused itch suppression. This study indicated that itch sensation was affected after CFA treatment, although negatively, and comprehensive but not specific suppression of different pruriceptors was observed after CFA treatment, suggesting that a unified adaptive change of increased pain and decreased itch will occur simultaneously under CFA-induced inflammatory conditions.

Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes.

Single-cell transcriptomic analysis is widely used to study human tumors. However it remains challenging to distinguish normal cell types in the tumor microenvironment from malignant cells and to resolve clonal substructure within the tumor. To address these challenges, we developed an integrative Bayesian segmentation approach called CopyKAT (Copynumber Karyotyping of Aneuploid Tumors) to estimate genomic copy number profiles at an average genomic resolution of 5Mb from read depth in high-throughput scRNA-seq data. We applied CopyKAT to analyze 46,501 single cells from 21 tumors, including triple-negative breast cancer, pancreatic ductal adenocarcinomas, anaplastic thyroid cancer, invasive ductal carcinoma and glioblastoma to accurately (98%) distinguish cancer cells from normal cell types. In three breast tumors, CopyKAT resolved clonal subpopulations that differed in the expression of cancer genes such as KRAS and signatures including EMT, DNA repair, apoptosis and hypoxia. These data show that CopyKAT can aid the analysis of scRNA-seq data in a variety of solid human tumors.

Bi-fated tendon-to-bone attachment cells are regulated by shared enhancers and KLF transcription factors.

The mechanical challenge of attaching elastic tendons to stiff bones is solved by the formation of a unique transitional tissue. Here, we show that murine tendon-to-bone attachment cells are bi-fated, activating a mixture of chondrocyte and tenocyte transcriptomes, under regulation of shared regulatory elements and Krüppel-like factors (KLFs) transcription factors. High-throughput bulk and single-cell RNA sequencing of humeral attachment cells revealed expression of hundreds of chondrogenic and tenogenic genes, which was validated by in situ hybridization and single-molecule ISH. ATAC sequencing showed that attachment cells share accessible intergenic chromatin areas with either tenocytes or chondrocytes. Epigenomic analysis revealed enhancer signatures for most of these regions. Transgenic mouse enhancer reporter assays verified the shared activity of some of these enhancers. Finally, integrative chromatin and motif analyses and transcriptomic data implicated KLFs as regulators of attachment cells. Indeed, blocking expression of both Klf2 and Klf4 in developing limb mesenchyme impaired their differentiation.