A Systematic Approach to Identify Candidate Transcription Factors that Control Cell Identity

IGF2BP2 Maintains Retinal Pigment Epithelium Homeostasis by Stabilizing PAX6 and OTX2

Purpose

N6-methyladenosine (m6A) methylation is a chemical modification that occurs on RNA molecules, where the hydrogen atom of adenine (A) nucleotides is replaced by a methyl group, forming N6-methyladenosine. This modification is a dynamic and reversible process that plays a crucial role in regulating various biological processes, including RNA stability, transport, translation, and degradation. Currently, there is a lack of research on the role of m6A modifications in maintaining the characteristics of RPE cells. m6A readers play a crucial role in executing the functions of m6A modifications, which prompted our investigation into their regulatory roles in the RPE.

Methods

Phagocytosis assays, immunofluorescence staining, flow cytometry experiments, β-galactosidase staining, and RNA sequencing (RNA-seq) were conducted to assess the functional and cellular characteristics changes in retinal pigment epithelium (RPE) cells following short-hairpin RNA-mediated knockdown of insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2). RNA-seq and ultraviolet crosslinking immunoprecipitation with high-throughput sequencing (HITS-CLIP) were employed to identify the target genes regulated by IGF2BP2. adeno-associated virus (AAV) subretinal injection was performed in 6- to 8-week-old C57 mice to reduce IGF2BP2 expression in the RPE, and the impact of IGF2BP2 knockdown on mouse visual function was assessed using immunofluorescence, quantitative real-time PCR, optical coherence tomography, and electroretinography.

Results

IGF2BP2 was found to have a pronounced effect on RPE phagocytosis. Subsequent in-depth exploration revealed that IGF2BP2 modulates the mRNA stability of PAX6 and OTX2, and the loss of IGF2BP2 induces inflammatory and aging phenotypes in RPE cells. IGF2BP2 knockdown impaired RPE function, leading to retinal dysfunction in vivo.

Conclusions

Our data suggest a crucial role of IGF2BP2 as an m6A reader in maintaining RPE homeostasis by regulating the stability of PAX6 and OTX2, making it a potential target for preventing the occurrence of retinal diseases related to RPE malfunction.

NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation

Transcription factor (TF) coordination plays a key role in target gene (TG) regulation via protein-protein interactions (PPIs) and DNA co-binding to regulatory elements. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF coordination and TG regulation of various cell types remains unclear. To address this, we have developed a novel computational approach, Network Regression Embeddings (NetREm), to reveal cell-type TF-TF coordination activities for TG regulation. NetREm leverages network-constrained regularization using prior knowledge of direct and/or indirect PPIs among TFs to analyze single-cell gene expression data. We test NetREm by simulation data and benchmark its performance in 4 real-world applications that have gold standard TF-TG networks available: mouse (mESCs) and simulated human (hESCs) embryonic stem (ESCs), human hematopoietic stem (HSCs), and mouse dendritic (mDCs) cells. Further, we use NetREm to prioritize valid novel TF-TF coordination links in human Peripheral Blood Mononuclear cell (PBMC) sub-types. We apply NetREm to analyze various cell types in both central (CNS) and peripheral (PNS) nerve system (NS) (e.g. neuronal, glial, Schwann cells (SCs)) as well as in Alzheimers disease (AD). Our findings uncover cell-type coordinating TFs and identify new TF-TG candidate links. We validate our top predictions using Cut&Run and knockout loss-of-function expression data in rat/mouse models and compare results with additional functional genomic data, including expression quantitative trait loci (eQTL) and Genome-Wide Association Studies (GWAS) to link genetic variants (single nucleotide polymorphisms (SNPs)) to TF coordination.

Reprograming skin fibroblasts into Sertoli cells: a patient-specific tool to understand effects of genetic variants on gonadal development

BACKGROUND

Disorders/differences of sex development (DSD) are congenital conditions in which the development of chromosomal, gonadal, or anatomical sex is atypical. With overlapping phenotypes and multiple genes involved, poor diagnostic yields are achieved for many of these conditions. The current DSD diagnostic regimen can be augmented by investigating transcriptome/proteome in vivo, but it is hampered by the unavailability of affected gonadal tissue at the relevant developmental stage. We try to mitigate this limitation by reprogramming readily available skin tissue-derived dermal fibroblasts into Sertoli cells (SC), which could then be deployed for different diagnostic strategies. SCs form the target cell type of choice because they act like an organizing center of embryonic gonadal development and many DSD arise when these developmental processes go awry.

METHODS

We employed a computational predictive algorithm for cell conversions called Mogrify to predict the transcription factors (TFs) required for direct reprogramming of human dermal fibroblasts into SCs. We established trans-differentiation culture conditions where stable transgenic expression of these TFs was achieved in 46, XY adult dermal fibroblasts using lentiviral vectors. The resulting Sertoli like cells (SLCs) were validated for SC phenotype using several approaches.

RESULTS

SLCs exhibited Sertoli-like morphological and cellular properties as revealed by morphometry and xCelligence cell behavior assays. They also showed Sertoli-specific expression of molecular markers such as SOX9, PTGDS, BMP4, or DMRT1 as revealed by IF imaging, RNAseq and qPCR. The SLC transcriptome shared about two thirds of its differentially expressed genes with a human adult SC transcriptome and expressed markers typical of embryonic SCs. Notably, SLCs lacked expression of most markers of other gonadal cell types such as Leydig, germ, peritubular myoid or granulosa cells.

CONCLUSIONS

The trans-differentiation method was applied to a variety of commercially available 46, XY fibroblasts derived from patients with DSD and to a 46, XX cell line. The DSD SLCs displayed altered levels of trans-differentiation in comparison to normal 46, XY-derived SLCs, thus showcasing the robustness of this new trans-differentiation model. Future applications could include using the SLCs to improve definitive diagnosis of DSD in patients with variants of unknown significance.

Cell reprogramming design by transfer learning of functional transcriptional networks

Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cells. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates, thereby generating a model of the network dynamics that can be transferred to specific reprogramming goals. The approach combines transcriptional responses to gene perturbations to minimize the difference between a given pair of initial and target transcriptional states. We demonstrate our approach's versatility by applying it to a microarray dataset comprising >9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of >10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases with decreasing developmental relatedness and that fewer genes are needed to progress along developmental paths than to regress. These findings establish a proof-of-concept for our approach to computationally design control strategies and provide insights into how gene regulatory networks govern phenotype.

Cell reprogramming design by transfer learning of functional transcriptional networks

Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cell behavior. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates to generate a model of the functional network dynamics that can be transferred to specific reprogramming goals. The approach additively combines transcriptional responses to gene perturbations (single-gene knockdowns and overexpressions) to minimize the transcriptional difference between a given pair of initial and target states. We demonstrate the flexibility of our approach by applying it to a microarray dataset comprising over 9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of over 10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an average AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases as the developmental relatedness decreases. We also show that fewer genes are needed to progress along developmental paths than to regress. Together, these findings establish a proof-of-concept for our approach to computationally design control strategies and demonstrate their ability to provide insights into the dynamics of gene regulatory networks.

SpecLoop predicts cell type-specific chromatin loop via transcription factor cooperation

Cell-type-Specific Chromatin Loops (CSCLs) are crucial for gene regulation and cell fate determination. However, the mechanisms governing their establishment remain elusive. Here, we present SpecLoop, a network regularization-based machine learning framework, to investigate the role of transcription factors (TFs) cooperation in CSCL formation. SpecLoop integrates multi-omics data, including gene expression, chromatin accessibility, sequence, protein-protein interaction, and TF binding motif data, to predict CSCLs and identify TF cooperations. Using high resolution Hi-C data as the gold standard, SpecLoop accurately predicts CSCL in GM12878, IMR90, HeLa-S3, K562, HUVEC, HMEC, and NHEK seven cell types, with the AUROC values ranging from 0.8645 to 0.9852 and AUPR values ranging from 0.8654 to 0.9734. Notably SpecLoop demonstrates improved accuracy in predicting long-distance CSCLs and identifies TF complexes with strong predictive ability. Our study systematically explores the TFs and TF pairs associated with CSCL through effective integration of diverse omics data. SpecLoop is freely available at https://github.com/AMSSwanglab/SpecLoop.

SinCMat: A single-cell-based method for predicting functional maturation transcription factors

A major goal of regenerative medicine is to generate tissue-specific mature and functional cells. However, current cell engineering protocols are still unable to systematically produce fully mature functional cells. While existing computational approaches aim at predicting transcription factors (TFs) for cell differentiation/reprogramming, no method currently exists that specifically considers functional cell maturation processes. To address this challenge, here, we develop SinCMat, a single-cell RNA sequencing (RNA-seq)-based computational method for predicting cell maturation TFs. Based on a model of cell maturation, SinCMat identifies pairs of identity TFs and signal-dependent TFs that co-target genes driving functional maturation. A large-scale application of SinCMat to the Mouse Cell Atlas and Tabula Sapiens accurately recapitulates known maturation TFs and predicts novel candidates. We expect SinCMat to be an important resource, complementary to preexisting computational methods, for studies aiming at producing functionally mature cells.

DIRECTEUR: transcriptome-based prediction of small molecules that replace transcription factors for direct cell conversion

MOTIVATION

Direct reprogramming (DR) is a process that directly converts somatic cells to target cells. Although DR via small molecules is safer than using transcription factors (TFs) in terms of avoidance of tumorigenic risk, the determination of DR-inducing small molecules is challenging.

RESULTS

Here we present a novel in silico method, DIRECTEUR, to predict small molecules that replace TFs for DR. We extracted DR-characteristic genes using transcriptome profiles of cells in which DR was induced by TFs, and performed a variant of simulated annealing to explore small molecule combinations with similar gene expression patterns with DR-inducing TFs. We applied DIRECTEUR to predicting combinations of small molecules that convert fibroblasts into neurons or cardiomyocytes, and were able to reproduce experimentally verified and functionally related molecules inducing the corresponding conversions. The proposed method is expected to be useful for practical applications in regenerative medicine.

AVAILABILITY AND IMPLEMENTATION

The code and data are available at the following link: https://github.com/HamanoLaboratory/DIRECTEUR.git.

Computational identification of key transcription factors for embryonic and postnatal Sox2+ dental epithelial stem cell

While many reptiles can replace their tooth throughout life, human loss the tooth replacement capability after formation of the permanent teeth. It was thought that the difference in tooth regeneration capability depends on the persistence of a specialized dental epithelial structure, the dental lamina that contains dental epithelial stem cells (DESC). Currently, we know very little about DESC such as what genes are expressed or its chromatin accessibility profile. Multiple markers of DESC have been proposed such as Sox2 and Lgr5 . Few single cell RNA-seq experiments have been performed previously, but no obvious DESC cluster was identified in these scRNA-seq datasets, possible due to that the expression level of DESC markers such as Sox2 and Lgr5 is too low or the percentage of DESC is too low in whole tooth. We utilize a mouse line Sox2-GFP to enrich Sox2+ DESC and use Smart-Seq2 protocol and ATAC-seq protocol to generate transcriptome profile and chromatin accessibility profile of P2 Sox2+ DESC. Additionally, we generate transcriptome profile and chromatin accessibility profile of E11.5 Sox2+ dental lamina cells. With transcriptome profile and chromatin accessibility profile, we systematically identify potential key transcription factors for E11.5 Sox2+ cells and P2 Sox2+ cells. We identified transcription factors including Pitx2, Id3, Pitx1, Tbx1, Trp63, Nkx2-3, Grhl3, Dlx2, Runx1, Nfix, Zfp536 , etc potentially formed the core transcriptional regulatory networks of Sox2+ DESC in both embryonic and postnatal stages.

Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON

Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.

Computational tools for inferring transcription factor activity

Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.

An extended transcription factor regulatory network controls hepatocyte identity

Cell identity is specified by a core transcriptional regulatory circuitry (CoRC), typically limited to a small set of interconnected cell-specific transcription factors (TFs). By mining global hepatic TF regulons, we reveal a more complex organization of the transcriptional regulatory network controlling hepatocyte identity. We show that tight functional interconnections controlling hepatocyte identity extend to non-cell-specific TFs beyond the CoRC, which we call hepatocyte identity (Hep-ID)CONNECT TFs. Besides controlling identity effector genes, Hep-IDCONNECT TFs also engage in reciprocal transcriptional regulation with TFs of the CoRC. In homeostatic basal conditions, this translates into Hep-IDCONNECT TFs being involved in fine tuning CoRC TF expression including their rhythmic expression patterns. Moreover, a role for Hep-IDCONNECT TFs in the control of hepatocyte identity is revealed in dedifferentiated hepatocytes where Hep-IDCONNECT TFs are able to reset CoRC TF expression. This is observed upon activation of NR1H3 or THRB in hepatocarcinoma or in hepatocytes subjected to inflammation-induced loss of identity. Our study establishes that hepatocyte identity is controlled by an extended array of TFs beyond the CoRC.

A cutting-edge strategy for spinal cord injury treatment: resident cellular transdifferentiation

Spinal cord injury causes varying degrees of motor and sensory function loss. However, there are no effective treatments for spinal cord repair following an injury. Moreover, significant preclinical advances in bioengineering and regenerative medicine have not yet been translated into effective clinical therapies. The spinal cord's poor regenerative capacity makes repairing damaged and lost neurons a critical treatment step. Reprogramming-based neuronal transdifferentiation has recently shown great potential in repair and plasticity, as it can convert mature somatic cells into functional neurons for spinal cord injury repair in vitro and in vivo, effectively halting the progression of spinal cord injury and promoting functional improvement. However, the mechanisms of the neuronal transdifferentiation and the induced neuronal subtypes are not yet well understood. This review analyzes the mechanisms of resident cellular transdifferentiation based on a review of the relevant recent literature, describes different molecular approaches to obtain different neuronal subtypes, discusses the current challenges and improvement methods, and provides new ideas for exploring therapeutic approaches for spinal cord injury.

Super-enhancers complexes zoom in transcription in cancer

Super-enhancers (SEs) consist of multiple typical enhancers enriched at high density with transcription factors, histone-modifying enzymes and cofactors. Oncogenic SEs promote tumorigenesis and malignancy by altering protein-coding gene expression and noncoding regulatory element function. Therefore, they play central roles in the treatment of cancer. Here, we review the structural characteristics, organization, identification, and functions of SEs and the underlying molecular mechanism by which SEs drive oncogenic transcription in tumor cells. We then summarize abnormal SE complexes, SE-driven coding genes, and noncoding RNAs involved in tumor development. In summary, we believe that SEs show great potential as biomarkers and therapeutic targets.

Single-Cell RNA Sequencing: Opportunities and Challenges for Studies on Corneal Biology in Health and Disease

The structure and major cell types of the multi-layer human cornea have been extensively studied. However, various cell states in specific cell types and key genes that define the cell states are not fully understood, hindering our comprehension of corneal homeostasis, related diseases, and therapeutic discovery. Single-cell RNA sequencing is a revolutionary and powerful tool for identifying cell states within tissues such as the cornea. This review provides an overview of current single-cell RNA sequencing studies on the human cornea, highlighting similarities and differences between them, and summarizing the key genes that define corneal cell states reported in these studies. In addition, this review discusses the opportunities and challenges of using single-cell RNA sequencing to study corneal biology in health and disease.

CNEReg Interprets Ruminant-specific Conserved Non-coding Elements by Developmental Gene Regulatory Network

The genetic information coded in DNA leads to trait innovation via a gene regulatory network (GRN) in development. Here, we developed a conserved non-coding element interpretation method to integrate multi-omics data into gene regulatory network (CNEReg) to investigate the ruminant multi-chambered stomach innovation. We generated paired expression and chromatin accessibility data during rumen and esophagus development in sheep, and revealed 1601 active ruminant-specific conserved non-coding elements (active-RSCNEs). To interpret the function of these active-RSCNEs, we defined toolkit transcription factors (TTFs) and modeled their regulation on rumen-specific genes via batteries of active-RSCNEs during development. Our developmental GRN revealed 18 TTFs and 313 active-RSCNEs regulating 7 rumen functional modules. Notably, 6 TTFs (OTX1, SOX21, HOXC8, SOX2, TP63, and PPARG), as well as 16 active-RSCNEs, functionally distinguished the rumen from the esophagus. Our study provides a systematic approach to understanding how gene regulation evolves and shapes complex traits by putting evo-devo concepts into practice with developmental multi-omics data.

Gene Therapy Activates Retinal Pigment Epithelium Cell Proliferation for Age-related Macular Degeneration in a Mouse Model

OBJECTIVE

Age-related macular degeneration (AMD) is a degenerative retinal disease. The degeneration or death of retinal pigment epithelium (RPE) cells is implicated in the pathogenesis of AMD. This study aimed to activate the proliferation of RPE cells in vivo by using an adeno-associated virus (AAV) vector encoding β-catenin to treat AMD in a mouse model.

METHODS

Mice were intravitreally injected with AAV2/8-Y733F-VMD2-β-catenin for 2 or 4 weeks, and β-catenin expression was measured using immunofluorescence staining, real-time quantitative reverse transcription polymerase chain reaction (PCR), and Western blotting. The function of β-catenin was determined using retinal flat mounts and laser-induced damage models. Finally, the safety of AAV2/8-Y733F-VMD2-β-catenin was evaluated by multiple intravitreal injections.

RESULTS

AAV2/8-Y733F-VMD2-β-catenin induced the expression of β-catenin in RPE cells. It activated the proliferation of RPE cells and increased cyclin D1 expression. It was beneficial to the recovery of laser-induced damage by activating the proliferation of RPE cells. Furthermore, it could induce apoptosis of RPE cells by increasing the expression of Trp53, Bax and caspase3 while decreasing the expression of Bcl-2.

CONCLUSION

AAV2/8-Y733F-VMD2-β-catenin increased β-catenin expression in RPE cells, activated RPE cell proliferation, and helped mice heal from laser-induced eye injury. Furthermore, it could induce the apoptosis of RPE cells. Therefore, it may be a safe approach for AMD treatment.

The complex network of transcription factors, immune checkpoint inhibitors and stemness features in colorectal cancer: A recent update

Cancer immunity is regulated by several mechanisms that include co-stimulatory and/or co-inhibitory molecules known as immune checkpoints expressed by the immune cells. In colorectal cancer (CRC), CTLA-4, LAG3, TIM-3 and PD-1 are the major co-inhibitory checkpoints involved in tumor development and progression. On the other hand, the deregulation of transcription factors and cancer stem cells activity plays a major role in the development of drug resistance and in the spread of metastatic disease in CRC. In this review, we describe how the modulation of such transcription factors affects the response of CRC to therapies. We also focus on the role of cancer stem cells in tumor metastasis and chemoresistance and discuss both preclinical and clinical approaches for targeting stem cells to prevent their tumorigenic effect. Finally, we provide an update on the clinical applications of immune checkpoint inhibitors in CRC and discuss the regulatory effects of transcription factors on the expression of the immune inhibitory checkpoints with specific focus on the PD-1 and PD-L1 molecules.

Epigenomic charting and functional annotation of risk loci in renal cell carcinoma

While the mutational and transcriptional landscapes of renal cell carcinoma (RCC) are well-known, the epigenome is poorly understood. We characterize the epigenome of clear cell (ccRCC), papillary (pRCC), and chromophobe RCC (chRCC) by using ChIP-seq, ATAC-Seq, RNA-seq, and SNP arrays. We integrate 153 individual data sets from 42 patients and nominate 50 histology-specific master transcription factors (MTF) to define RCC histologic subtypes, including EPAS1 and ETS-1 in ccRCC, HNF1B in pRCC, and FOXI1 in chRCC. We confirm histology-specific MTFs via immunohistochemistry including a ccRCC-specific TF, BHLHE41. FOXI1 overexpression with knock-down of EPAS1 in the 786-O ccRCC cell line induces transcriptional upregulation of chRCC-specific genes, TFCP2L1, ATP6V0D2, KIT, and INSRR, implicating FOXI1 as a MTF for chRCC. Integrating RCC GWAS risk SNPs with H3K27ac ChIP-seq and ATAC-seq data reveals that risk-variants are significantly enriched in allelically-imbalanced peaks. This epigenomic atlas in primary human samples provides a resource for future investigation.

The LHX2-OTX2 transcriptional regulatory module controls retinal pigmented epithelium differentiation and underlies genetic risk for age-related macular degeneration

Tissue-specific transcription factors (TFs) control the transcriptome through an association with noncoding regulatory regions (cistromes). Identifying the combination of TFs that dictate specific cell fate, their specific cistromes and examining their involvement in complex human traits remain a major challenge. Here, we focus on the retinal pigmented epithelium (RPE), an essential lineage for retinal development and function and the primary tissue affected in age-related macular degeneration (AMD), a leading cause of blindness. By combining mechanistic findings in stem-cell-derived human RPE, in vivo functional studies in mice and global transcriptomic and proteomic analyses, we revealed that the key developmental TFs LHX2 and OTX2 function together in transcriptional module containing LDB1 and SWI/SNF (BAF) to regulate the RPE transcriptome. Importantly, the intersection between the identified LHX2-OTX2 cistrome with published expression quantitative trait loci, ATAC-seq data from human RPE, and AMD genome-wide association study (GWAS) data, followed by functional validation using a reporter assay, revealed a causal genetic variant that affects AMD risk by altering TRPM1 expression in the RPE through modulation of LHX2 transcriptional activity on its promoter. Taken together, the reported cistrome of LHX2 and OTX2, the identified downstream genes and interacting co-factors reveal the RPE transcription module and uncover a causal regulatory risk single-nucleotide polymorphism (SNP) in the multifactorial common blinding disease AMD.

A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion

Cellular conversion can be induced by perturbing a handful of key transcription factors (TFs). Replacement of direct manipulation of key TFs with chemical compounds offers a less laborious and safer strategy to drive cellular conversion for regenerative medicine. Nevertheless, identifying optimal chemical compounds currently requires large-scale screening of chemical libraries, which is resource intensive. Existing computational methods aim at predicting cell conversion TFs, but there are no methods for identifying chemical compounds targeting these TFs. Here, we develop a single cell-based platform (SiPer) to systematically prioritize chemical compounds targeting desired TFs to guide cellular conversions. SiPer integrates a large compendium of chemical perturbations on non-cancer cells with a network model and predicted known and novel chemical compounds in diverse cell conversion examples. Importantly, we applied SiPer to develop a highly efficient protocol for human hepatic maturation. Overall, SiPer provides a valuable resource to efficiently identify chemical compounds for cell conversion.

Reprogramming cell fates towards novel cancer immunotherapies

Recent advances in our understanding of host immune and cancer cells interactions have made immunotherapy a prominent choice in cancer treatment. Despite such promise, cell-based immunotherapies remain inapplicable to many patients due to severe limitations in the availability and quality of immune cells isolated from donors. Reprogramming technologies that facilitate the engineering of cell types of interest, are emerging as a putative solution to such challenges. Here we focus on the recent progress being made in reprogramming technologies with respect to the immune system and their potential for clinical applications.

Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework

Advanced computational methods exploit gene expression and epigenetic datasets to predict gene regulatory networks controlled by transcription factors (TFs). These methods have identified cell fate determining TFs but require large amounts of reference data and experimental expertise. Here, we present an easy to use network-based computational framework that exploits enhancers defined by bidirectional transcription, using as sole input CAGE sequencing data to correctly predict TFs key to various human cell types. Next, we applied this Analysis Algorithm for Networks Specified by Enhancers based on CAGE (ANANSE-CAGE) to predict TFs driving red and white blood cell development, and THP-1 leukemia cell immortalization. Further, we predicted TFs that are differentially important to either cell line- or primary- associated MLL-AF9-driven gene programs, and in primary MLL-AF9 acute leukemia. Our approach identified experimentally validated as well as thus far unexplored TFs in these processes. ANANSE-CAGE will be useful to identify transcription factors that are key to any cell fate change using only CAGE-seq data as input.

Increased expression of bone/cartilage-associated genes and core transcription factors in keloids by RNA sequencing

Fibroblasts in keloids undergo cell identity transition with altered transcriptional characteristics. However, the core transcription factors driving this cellular reprogramming remain largely unknown. Here, we report the results of transcriptional profiling from 48 keloid and 24 control dermal tissues. We identified 1187 upregulated differentially expressed genes (foldchange > 2, false discovery rate < 0.05) in keloids, which were mainly enriched in extracellular matrix organization and bone/cartilage development, with significantly increased expression of bone/cartilage-associated collagens (COL5A1, COL10A1, and COL11A1) and glycoproteins (ACAN, COMP, and SPARC). Deconvolution analysis also revealed significantly increased composition of osteoblasts in keloid dermis. A total of 92 upregulated transcription factors were screened out from differentially expressed genes and mainly enriched in transcription process and skeleton development. Additional sequencing of six keloid individuals with multiple regions and intersection further narrow the list with 10 transcription factors. Finally, AEBP1, CREB3L1, RUNX2, and ZNF469 have been identified as candidate core regulators in promoting the gaining of bone/cartilage-like characteristics in keloids. RNA-sequencing of full-skin keloids consolidated the existence of these four transcription factors. Immunohistochemistry was employed to verify the expression of AEBP1, CREB3L1, RUNX2, and ZNF469 in keloid fibroblasts. In conclusion, we bioinformatically discovered the increased expression of bone/cartilage-associated genes and candidate core transcription factors in keloids. Our findings promise to provide molecular clues to develop novel therapeutic modalities against skin fibrosis.

Induced retinal pigment epithelial cells with anti-epithelial-to-mesenchymal transition ability delay retinal degeneration

The hostile microenvironment of the retina in patients with age-related macular degeneration (AMD) may trigger epithelial-to-mesenchymal transition (EMT) of grafted retinal pigment epithelial (RPE) cells, thus attenuating the therapeutic outcome. Here, we transformed human dedifferentiated induced pluripotent stem cell-derived RPE (iPSC-RPE) cells into induced RPE (iRPE) cells using a cocktail of four transcription factors (TFs)—CRX, MITF-A, NR2E1, and C-MYC. These critical TFs maintained the epithelial property of iRPE cells by regulating the expression of bmp7, forkhead box f2, lin7a, and pard6b, and conferred resistance to TGF-β-induced EMT in iRPE cells by targeting ppm1a. The iRPE cells with Tet-on system-regulated c-myc expression exhibited EMT resistance and better therapeutic function compared with iPSC-RPE cells in rat AMD model. Our study demonstrates that endowing RPE cells with anti-EMT property avoids the risk of EMT after cells are grafted into the subretinal space, and it may provide a suitable candidate for AMD treatment.

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CRX, MITF-A, NR2E1, and C-MYC transform De-iPSC-RPE cells into iRPE cells

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iRPE cells have resistance to TGF-β-induced EMT

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BMP7, FOXF2, LIN7A, PARD6B, and PPM1A mediate the functions of TFs in iRPE cells

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iRPE cells have better retinal protective function than iPSC-RPE cells

Direct conversion of human umbilical cord mesenchymal stem cells into retinal pigment epithelial cells for treatment of retinal degeneration

Age-related macular degeneration (AMD) is a major vision-threatening disease. Although mesenchymal stem cells (MSCs) exhibit beneficial neural protective effects, their limited differentiation capacity in vivo attenuates their therapeutic function. Therefore, the differentiation of MSCs into retinal pigment epithelial (RPE) cells in vitro and their subsequent transplantation into the subretinal space is expected to improve the outcome of cell therapy. Here, we transdifferentiated human umbilical cord MSCs (hUCMSCs) into induced RPE (iRPE) cells using a cocktail of five transcription factors (TFs): CRX, NR2E1, C-MYC, LHX2, and SIX6. iRPE cells exhibited RPE specific properties, including phagocytic ability, epithelial polarity, and gene expression profile. In addition, high expression of PTPN13 in iRPE cells endows them with an epithelial-to-mesenchymal transition (EMT)-resistant capacity through dephosphorylating syntenin1, and subsequently promoting the internalization and degradation of transforming growth factor-β receptors. After grafting into the subretinal space of the sodium iodate-induced rat AMD model, iRPE cells demonstrated a better therapeutic function than hUCMSCs. These results suggest that hUCMSC-derived iRPE cells may be promising candidates to reverse AMD pathophysiology.

Probabilistic boolean networks predict transcription factor targets to induce transdifferentiation

We developed a computational approach to find the best intervention to achieve transcription factor (TF) mediated transdifferentiation. We construct probabilistic Boolean networks (PBNs) from single-cell RNA sequencing data of two different cell states to model hematopoietic transcription factors cross-talk. This was achieved by a “sampled network” approach, which enabled us to construct large networks. The interventions to induce transdifferentiation consisted of permanently activating or deactivating each of the TFs and determining the probability mass transfer of steady-state probabilities from the departure to the destination cell type or state. Our findings support the common assumption that TFs that are differentially expressed between the two cell types are the best intervention points to achieve transdifferentiation. TFs whose interventions are found to transdifferentiate progenitor B cells into monocytes include EBF1 down-regulation, CEBPB up-regulation, TCF3 down-regulation, and STAT3 up-regulation.

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Differentially expressed transcription factors are the best for transdifferentiation

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Probabilistic Boolean networks (PBNs) are used to model transdifferentiation using the scRNAseq data at one time point

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A new approach works for a large number of network nodes

Ranking reprogramming factors for cell differentiation

Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. Here we examine the success rate of methods and data for differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBseq, AME, DREME, HOMER, KMAC, diffTF and DeepAccess) to discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that use gene expression, biological networks and chromatin accessibility data, and comprehensively test parameter and preprocessing of input data to optimize performance. We find the best factor identification methods can identify an average of 50-60% of reprogramming factors within the top ten candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF have higher correlation with the ranked significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and diffTF are optimal methods for transcription factor recovery that will allow for systematic prioritization of transcription factor candidates to aid in the design of new reprogramming protocols.

Annotating regulatory elements by heterogeneous network embedding

MOTIVATION

Regulatory elements (REs), such as enhancers and promoters, are known as regulatory sequences functional in a heterogeneous regulatory network to control gene expression by recruiting transcription regulators and carrying genetic variants in a context specific way. Annotating those REs relies on costly and labor-intensive next-generation sequencing and RNA-guided editing technologies in many cellular contexts.

RESULTS

We propose a systematic Gene Ontology Annotation method for Regulatory Elements (RE-GOA) by leveraging the powerful word embedding in natural language processing. We first assemble a heterogeneous network by integrating context specific regulations, protein-protein interactions and gene ontology (GO) terms. Then we perform network embedding and associate regulatory elements with GO terms by assessing their similarity in a low dimensional vector space. With three applications, we show that RE-GOA outperforms existing methods in annotating TFs' binding sites from ChIP-seq data, in functional enrichment analysis of differentially accessible peaks from ATAC-seq data, and in revealing genetic correlation among phenotypes from their GWAS summary statistics data.

AVAILABILITY AND IMPLEMENTATION

The source code and the systematic RE annotation for human and mouse are available at https://github.com/AMSSwanglab/RE-GOA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TRANSDIRE: data-driven direct reprogramming by a pioneer factor-guided trans-omics approach

MOTIVATION

Direct reprogramming involves the direct conversion of fully differentiated mature cell types into various other cell types while bypassing an intermediate pluripotent state (e.g. induced pluripotent stem cells). Cell differentiation by direct reprogramming is determined by two types of transcription factors (TFs): pioneer factors (PFs) and cooperative TFs. PFs have the distinct ability to open chromatin aggregations, assemble a collective of cooperative TFs and activate gene expression. The experimental determination of two types of TFs is extremely difficult and costly.

RESULTS

In this study, we developed a novel computational method, TRANSDIRE (TRANS-omics-based approach for DIrect REprogramming), to predict the TFs that induce direct reprogramming in various human cell types using multiple omics data. In the algorithm, potential PFs were predicted based on low signal chromatin regions, and the cooperative TFs were predicted through a trans-omics analysis of genomic data (e.g. enhancers), transcriptome data (e.g. gene expression profiles in human cells), epigenome data (e.g. chromatin immunoprecipitation sequencing data) and interactome data. We applied the proposed methods to the reconstruction of TFs that induce direct reprogramming from fibroblasts to six other cell types: hepatocytes, cartilaginous cells, neurons, cardiomyocytes, pancreatic cells and Paneth cells. We demonstrated that the methods successfully predicted TFs for most cell conversions with high accuracy. Thus, the proposed methods are expected to be useful for various practical applications in regenerative medicine.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at the following website: http://figshare.com/s/b653781a5b9e6639972b.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Epigenetics, Enhancer Function and 3D Chromatin Organization in Reprogramming to Pluripotency

Genome architecture, epigenetics and enhancer function control the fate and identity of cells. Reprogramming to induced pluripotent stem cells (iPSCs) changes the transcriptional profile and chromatin landscape of the starting somatic cell to that of the pluripotent cell in a stepwise manner. Changes in the regulatory networks are tightly regulated during normal embryonic development to determine cell fate, and similarly need to function in cell fate control during reprogramming. Switching off the somatic program and turning on the pluripotent program involves a dynamic reorganization of the epigenetic landscape, enhancer function, chromatin accessibility and 3D chromatin topology. Within this context, we will review here the current knowledge on the processes that control the establishment and maintenance of pluripotency during somatic cell reprogramming.

Bioinformatic Design of Dendritic Cell-Specific Synthetic Promoters

Next-generation DNA vectors for cancer immunotherapies and vaccine development require promoters eliciting predefined transcriptional activities specific to target cell types, such as dendritic cells (DCs), which underpin immune response. In this study, we describe the de novo design of DC-specific synthetic promoters via in silico assembly of cis-transcription factor response elements (TFREs) that harness the DC transcriptional landscape. Using computational genome mining approaches, candidate TFREs were identified within promoter sequences of highly expressed DC-specific genes or those exhibiting an upregulated expression during DC maturation. Individual TFREs were then screened in vitro in a target DC line and off-target cell lines derived from skeletal muscle, fibroblast, epithelial, and endothelial cells using homotypic (TFRE repeats in series) reporter constructs. Based on these data, a library of heterotypic promoter assemblies varying in the TFRE composition, copy number, and sequential arrangement was constructed and tested in vitro to identify DC-specific promoters. Analysis of the transcriptional activity and specificity of these promoters unraveled underlying design rules, primarily TFRE composition, which govern the DC-specific synthetic promoter activity. Using these design rules, a second library of exclusively DC-specific promoters exhibiting varied transcriptional activities was generated. All DC-specific synthetic promoter assemblies exhibited >5-fold activity in the target DC line relative to off-target cell lines, with transcriptional activities ranging from 8 to 67% of the nonspecific human cytomegalovirus (hCMV-IE1) promoter. We show that bioinformatic analysis of a mammalian cell transcriptional landscape is an effective strategy for de novo design of cell-type-specific synthetic promoters with precisely controllable transcriptional activities.

H3K27ac nucleosomes facilitate HMGN localization at regulatory sites to modulate chromatin binding of transcription factors

Nucleosomes containing acetylated H3K27 are a major epigenetic mark of active chromatin and identify cell-type specific chromatin regulatory regions which serve as binding sites for transcription factors. Here we show that the ubiquitous nucleosome binding proteins HMGN1 and HMGN2 bind preferentially to H3K27ac nucleosomes at cell-type specific chromatin regulatory regions. HMGNs bind directly to the acetylated nucleosome; the H3K27ac residue and linker DNA facilitate the preferential binding of HMGNs to the modified nucleosomes. Loss of HMGNs increases the levels of H3K27me3 and the histone H1 occupancy at enhancers and promoters and alters the interaction of transcription factors with chromatin. These experiments indicate that the H3K27ac epigenetic mark enhances the interaction of architectural protein with chromatin regulatory sites and identify determinants that facilitate the localization of HMGN proteins at regulatory sites to modulate cell-type specific gene expression.

Inducing human retinal pigment epithelium-like cells from somatic tissue

Regenerative medicine relies on basic research outcomes that are only practical when cost effective. The human eyeball requires the retinal pigment epithelium (RPE) to interface the neural retina and the choroid at large. Millions of people suffer from age-related macular degeneration (AMD), a blinding multifactor genetic disease among RPE degradation pathologies. Recently, autologous pluripotent stem-cell-derived RPE cells were prohibitively expensive due to time; therefore, we developed a faster reprogramming system. We stably induced RPE-like cells (iRPE) from human fibroblasts (Fibs) by conditional overexpression of both broad plasticity and lineage-specific transcription factors (TFs). iRPE cells displayed critical RPE benchmarks and significant in vivo integration in transplanted retinas. Herein, we detail the iRPE system with comprehensive single-cell RNA sequencing (scRNA-seq) profiling to interpret and characterize its best cells. We anticipate that our system may enable robust retinal cell induction for basic research and affordable autologous human RPE tissue for regenerative cell therapy.

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Human Fibs reprogrammed to stable RPE-like cells

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Reprogramming factors selected for pioneering, plasticity, lineage, and target cell

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Nicotinamide (NIC) and Chetomin (CTM) improved the reprogramming outcomes

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scRNA-seq analysis identifies high-quality subpopulation resembling model cells

Computational Methods to Identify Cell-Fate Determinants, Identity Transcription Factors, and Niche-Induced Signaling Pathways for Stem Cell Research

The large-scale development of high-throughput sequencing technologies has not only allowed the generation of reliable omics data related to various regulatory layers but also the development of novel computational models in the field of stem cell research. These computational approaches have enabled the disentangling of a complex interplay between these interrelated layers of regulation by interpreting large quantities of biomedical data in a systematic way. In the context of stem cell research, network modeling of complex gene-gene interactions has been successfully used for understanding the mechanisms underlying stem cell differentiation and cellular conversion. Notably, it has proven helpful for predicting cell-fate determinants and signaling molecules controlling such processes. This chapter will provide an overview of various computational approaches that rely on single-cell and/or bulk RNA sequencing data for elucidating the molecular underpinnings of cell subpopulation identities, lineage specification, and the process of cell-fate decisions. Furthermore, we discuss how these computational methods provide the right framework for computational modeling of biological systems in order to address long-standing challenges in the stem cell field by guiding experimental efforts in stem cell research and regenerative medicine.

OUP accepted manuscript

Recent advances in direct cell reprogramming have made possible the conversion of one cell type to another cell type, offering a potential cell-based treatment to many major diseases. Despite much attention, substantial roadblocks remain including the inefficiency in the proportion of reprogrammed cells of current experiments, and the requirement of a significant amount of time and resources. To this end, several computational algorithms have been developed with the goal of guiding the hypotheses to be experimentally validated. These approaches can be broadly categorized into two main types: transcription factor identification methods which aim to identify candidate transcription factors for a desired cell conversion, and transcription factor perturbation methods which aim to simulate the effect of a transcription factor perturbation on a cell state. The transcription factor perturbation methods can be broken down into Boolean networks, dynamical systems and regression models. We summarize the contributions and limitations of each method and discuss the innovation that single cell technologies are bringing to these approaches and we provide a perspective on the future direction of this field.

Transcriptional and epigenetic regulation in thymic epithelial cells

The thymus is required for the development of both adaptive and innate-like T cell subsets. There is keen interest in manipulating thymic function for therapeutic purposes in circumstances of autoimmunity, immunodeficiency, and for purposes of immunotherapy. Within the thymus, thymic epithelial cells play essential roles in directing T cell development. Several transcription factors are known to be essential for thymic epithelial cell development and function, and a few transcription factors have been studied in considerable detail. However, the role of many other transcription factors is less well understood. Further, it is likely that roles exist for other transcription factors not yet known to be important in thymic epithelial cells. Recent progress in understanding of thymic epithelial cell heterogeneity has provided some new insight into transcriptional requirements in subtypes of thymic epithelial cells. However, it is unknown whether progenitors of thymic epithelial cells exist in the adult thymus, and consequently, developmental relationships linking putative precursors with differentiated cell types are poorly understood. While we do not presently possess a clear understanding of stage-specific requirements for transcription factors in thymic epithelial cells, new single-cell transcriptomic and epigenomic technologies should enable rapid progress in this field. Here, we review our current knowledge of transcription factors involved in the development, maintenance, and function of thymic epithelial cells, and the mechanisms by which they act.

Predicting master transcription factors from pan-cancer expression data

Critical developmental “master transcription factors” (MTFs) can be subverted during tumorigenesis to control oncogenic transcriptional programs. Current approaches to identifying MTFs rely on ChIP-seq data, which is unavailable for many cancers. We developed the CaCTS (Cancer Core Transcription factor Specificity) algorithm to prioritize candidate MTFs using pan-cancer RNA sequencing data. CaCTS identified candidate MTFs across 34 tumor types and 140 subtypes including predictions for cancer types/subtypes for which MTFs are unknown, including e.g. PAX8, SOX17, and MECOM as candidates in ovarian cancer (OvCa). In OvCa cells, consistent with known MTF properties, these factors are required for viability, lie proximal to superenhancers, co-occupy regulatory elements globally, co-bind loci encoding OvCa biomarkers, and are sensitive to pharmacologic inhibition of transcription. Our predictions of MTFs, especially for tumor types with limited understanding of transcriptional drivers, pave the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

Taiji-reprogram: a framework to uncover cell-type specific regulators and predict cellular reprogramming cocktails

Cellular reprogramming is a promising technology to develop disease models and cell-based therapies. Identification of the key regulators defining the cell type specificity is pivotal to devising reprogramming cocktails for successful cell conversion but remains a great challenge. Here, we present a systems biology approach called Taiji-reprogram to efficiently uncover transcription factor (TF) combinations for conversion between 154 diverse cell types or tissues. This method integrates the transcriptomic and epigenomic data to construct cell-type specific genetic networks and assess the global importance of TFs in the network. Comparative analysis across cell types revealed TFs that are specifically important in a particular cell type and often tightly associated with cell-type specific functions. A systematic search of TFs with differential importance in the source and target cell types uncovered TF combinations for desired cell conversion. We have shown that Taiji-reprogram outperformed the existing methods to better recover the TFs in the experimentally validated reprogramming cocktails. This work not only provides a comprehensive catalog of TFs defining cell specialization but also suggests TF combinations for direct cell conversion.

Single-Cell Genomics: Catalyst for Cell Fate Engineering

As we near a complete catalog of mammalian cell types, the capability to engineer specific cell types on demand would transform biomedical research and regenerative medicine. However, the current pace of discovering new cell types far outstrips our ability to engineer them. One attractive strategy for cellular engineering is direct reprogramming, where induction of specific transcription factor (TF) cocktails orchestrates cell state transitions. Here, we review the foundational studies of TF-mediated reprogramming in the context of a general framework for cell fate engineering, which consists of: discovering new reprogramming cocktails, assessing engineered cells, and revealing molecular mechanisms. Traditional bulk reprogramming methods established a strong foundation for TF-mediated reprogramming, but were limited by their small scale and difficulty resolving cellular heterogeneity. Recently, single-cell technologies have overcome these challenges to rapidly accelerate progress in cell fate engineering. In the next decade, we anticipate that these tools will enable unprecedented control of cell state.

Responsiveness to perturbations is a hallmark of transcription factors that maintain cell identity in vitro

Identifying the particular transcription factors that maintain cell type in vitro is important for manipulating cell type. Identifying such transcription factors by their cell-type-specific expression or their involvement in developmental regulation has had limited success. We hypothesized that because cell type is often resilient to perturbations, the transcriptional response to perturbations would reveal identity-maintaining transcription factors. We developed perturbation panel profiling (P3) as a framework for perturbing cells across many conditions and measuring gene expression responsiveness transcriptome-wide. In human iPSC-derived cardiac myocytes, P3 showed that transcription factors important for cardiac myocyte differentiation and maintenance were among the most frequently upregulated (most responsive). We reasoned that one function of responsive genes may be to maintain cellular identity. We identified responsive transcription factors in fibroblasts using P3 and found that suppressing their expression led to enhanced reprogramming. We propose that responsiveness to perturbations is a property of transcription factors that help maintain cellular identity in vitro. A record of this paper's transparent peer review process is included in the supplemental information.

ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination

Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate transitions using differential networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

MIMIC: an optimization method to identify cell type-specific marker panel for cell sorting

Multi-omics data allow us to select a small set of informative markers for the discrimination of specific cell types and study of cellular heterogeneity. However, it is often challenging to choose an optimal marker panel from the high-dimensional molecular profiles for a large amount of cell types. Here, we propose a method called Mixed Integer programming Model to Identify Cell type-specific marker panel (MIMIC). MIMIC maintains the hierarchical topology among different cell types and simultaneously maximizes the specificity of a fixed number of selected markers. MIMIC was benchmarked on the mouse ENCODE RNA-seq dataset, with 29 diverse tissues, for 43 surface markers (SMs) and 1345 transcription factors (TFs). MIMIC could select biologically meaningful markers and is robust for different accuracy criteria. It shows advantages over the standard single gene-based approaches and widely used dimensional reduction methods, such as multidimensional scaling and t-SNE, both in accuracy and in biological interpretation. Furthermore, the combination of SMs and TFs achieves better specificity than SMs or TFs alone. Applying MIMIC to a large collection of 641 RNA-seq samples covering 231 cell types identifies a panel of TFs and SMs that reveal the modularity of cell type association networks. Finally, the scalability of MIMIC is demonstrated by selecting enhancer markers from mouse ENCODE data. MIMIC is freely available at https://github.com/MengZou1/MIMIC.

Promoter G-quadruplexes and transcription factors cooperate to shape the cell type-specific transcriptome

Cell identity is maintained by activation of cell-specific gene programs, regulated by epigenetic marks, transcription factors and chromatin organization. DNA G-quadruplex (G4)-folded regions in cells were reported to be associated with either increased or decreased transcriptional activity. By G4-ChIP-seq/RNA-seq analysis on liposarcoma cells we confirmed that G4s in promoters are invariably associated with high transcription levels in open chromatin. Comparing G4 presence, location and transcript levels in liposarcoma cells to available data on keratinocytes, we showed that the same promoter sequences of the same genes in the two cell lines had different G4-folding state: high transcript levels consistently associated with G4-folding. Transcription factors AP-1 and SP1, whose binding sites were the most significantly represented in G4-folded sequences, coimmunoprecipitated with their G4-folded promoters. Thus, G4s and their associated transcription factors cooperate to determine cell-specific transcriptional programs, making G4s to strongly emerge as new epigenetic regulators of the transcription machinery.

Single-cell RNA-seq reveals fibroblast heterogeneity and increased mesenchymal fibroblasts in human fibrotic skin diseases

Fibrotic skin disease represents a major global healthcare burden, characterized by fibroblast hyperproliferation and excessive accumulation of extracellular matrix. Fibroblasts are found to be heterogeneous in multiple fibrotic diseases, but fibroblast heterogeneity in fibrotic skin diseases is not well characterized. In this study, we explore fibroblast heterogeneity in keloid, a paradigm of fibrotic skin diseases, by using single-cell RNA-seq. Our results indicate that keloid fibroblasts can be divided into 4 subpopulations: secretory-papillary, secretory-reticular, mesenchymal and pro-inflammatory. Interestingly, the percentage of mesenchymal fibroblast subpopulation is significantly increased in keloid compared to normal scar. Functional studies indicate that mesenchymal fibroblasts are crucial for collagen overexpression in keloid. Increased mesenchymal fibroblast subpopulation is also found in another fibrotic skin disease, scleroderma, suggesting this is a broad mechanism for skin fibrosis. These findings will help us better understand skin fibrotic pathogenesis, and provide potential targets for fibrotic disease therapies.

Employing core regulatory circuits to define cell identity

The interplay between extrinsic signaling and downstream gene networks controls the establishment of cell identity during development and its maintenance in adult life. Advances in next-generation sequencing and single-cell technologies have revealed additional layers of complexity in cell identity. Here, we review our current understanding of transcription factor (TF) networks as key determinants of cell identity. We discuss the concept of the core regulatory circuit as a set of TFs and interacting factors that together define the gene expression profile of the cell. We propose the core regulatory circuit as a comprehensive conceptual framework for defining cellular identity and discuss its connections to cell function in different contexts.

hReg-CNCC reconstructs a regulatory network in human cranial neural crest cells and annotates variants in a developmental context

Cranial Neural Crest Cells (CNCC) originate at the cephalic region from forebrain, midbrain and hindbrain, migrate into the developing craniofacial region, and subsequently differentiate into multiple cell types. The entire specification, delamination, migration, and differentiation process is highly regulated and abnormalities during this craniofacial development cause birth defects. To better understand the molecular networks underlying CNCC, we integrate paired gene expression & chromatin accessibility data and reconstruct the genome-wide human Regulatory network of CNCC (hReg-CNCC). Consensus optimization predicts high-quality regulations and reveals the architecture of upstream, core, and downstream transcription factors that are associated with functions of neural plate border, specification, and migration. hReg-CNCC allows us to annotate genetic variants of human facial GWAS and disease traits with associated cis-regulatory modules, transcription factors, and target genes. For example, we reveal the distal and combinatorial regulation of multiple SNPs to core TF ALX1 and associations to facial distances and cranial rare disease. In addition, hReg-CNCC connects the DNA sequence differences in evolution, such as ultra-conserved elements and human accelerated regions, with gene expression and phenotype. hReg-CNCC provides a valuable resource to interpret genetic variants as early as gastrulation during embryonic development. The network resources are available at https://github.com/AMSSwanglab/hReg-CNCC .

A comprehensive library of human transcription factors for cell fate engineering

Human pluripotent stem cells (hPSCs) offer an unprecedented opportunity to model diverse cell types and tissues. To enable systematic exploration of the programming landscape mediated by transcription factors (TFs), we present the Human TFome, a comprehensive library containing 1,564 TF genes and 1,732 TF splice isoforms. By screening the library in three hPSC lines, we discovered 290 TFs, including 241 that were previously unreported, that induce differentiation in 4 days without alteration of external soluble or biomechanical cues. We used four of the hits to program hPSCs into neurons, fibroblasts, oligodendrocytes and vascular endothelial-like cells that have molecular and functional similarity to primary cells. Our cell-autonomous approach enabled parallel programming of hPSCs into multiple cell types simultaneously. We also demonstrated orthogonal programming by including oligodendrocyte-inducible hPSCs with unmodified hPSCs to generate cerebral organoids, which expedited in situ myelination. Large-scale combinatorial screening of the Human TFome will complement other strategies for cell engineering based on developmental biology and computational systems biology.

Reprogramming of the FOXA1 cistrome in treatment-emergent neuroendocrine prostate cancer

Lineage plasticity, the ability of a cell to alter its identity, is an increasingly common mechanism of adaptive resistance to targeted therapy in cancer. An archetypal example is the development of neuroendocrine prostate cancer (NEPC) after treatment of prostate adenocarcinoma (PRAD) with inhibitors of androgen signaling. NEPC is an aggressive variant of prostate cancer that aberrantly expresses genes characteristic of neuroendocrine (NE) tissues and no longer depends on androgens. Here, we investigate the epigenomic basis of this resistance mechanism by profiling histone modifications in NEPC and PRAD patient-derived xenografts (PDXs) using chromatin immunoprecipitation and sequencing (ChIP-seq). We identify a vast network of cis-regulatory elements (N~15,000) that are recurrently activated in NEPC. The FOXA1 transcription factor (TF), which pioneers androgen receptor (AR) chromatin binding in the prostate epithelium, is reprogrammed to NE-specific regulatory elements in NEPC. Despite loss of dependence upon AR, NEPC maintains FOXA1 expression and requires FOXA1 for proliferation and expression of NE lineage-defining genes. Ectopic expression of the NE lineage TFs ASCL1 and NKX2-1 in PRAD cells reprograms FOXA1 to bind to NE regulatory elements and induces enhancer activity as evidenced by histone modifications at these sites. Our data establish the importance of FOXA1 in NEPC and provide a principled approach to identifying cancer dependencies through epigenomic profiling.