Multiplexed Engineering and Analysis of Combinatorial Enhancer Activity in Single Cells

Transcriptional Regulation of the Hippo Pathway: Current Understanding and Insights from Single-Cell Technologies

The Hippo pathway regulates tissue homeostasis in normal development and drives oncogenic processes. In this review, we extensively discuss how YAP/TAZ/TEAD cooperate with other master transcription factors and epigenetic cofactors to orchestrate a broad spectrum of transcriptional responses. Even though these responses are often context- and lineage-specific, we do not have a good understanding of how such precise and specific transcriptional control is achieved—whether they are driven by differences in TEAD paralogs, or recruitment of cofactors to tissue-specific enhancers. We believe that emerging single-cell technologies would enable a granular understanding of how the Hippo pathway influences cell fate and drives oncogenic processes, ultimately allowing us to design better pharmacological agents against TEADs and identify robust pharmacodynamics markers of Hippo pathway inhibition.

Tutorial: design and execution of CRISPR in vivo screens

Here we provide a detailed tutorial on CRISPR in vivo screening. Using the mouse as the model organism, we introduce a range of CRISPR tools and applications, delineate general considerations for 'transplantation-based' or 'direct in vivo' screening design, and provide details on technical execution, sequencing readouts, computational analyses and data interpretation. In vivo screens face unique pitfalls and limitations, such as delivery issues or library bottlenecking, which must be counteracted to avoid screening failure or flawed conclusions. A broad variety of in vivo phenotypes can be interrogated such as organ development, hematopoietic lineage decision and evolutionary licensing in oncogenesis. We describe experimental strategies to address various biological questions and provide an outlook on emerging CRISPR applications, such as genetic interaction screening. These technological advances create potent new opportunities to dissect the molecular underpinnings of complex organismal phenotypes.

Inhibition of a Chromatin and Transcription Modulator, SLTM, Increases HIV-1 Reactivation Identified by a CRISPR Inhibition Screen

Despite effective antiretroviral therapy, HIV-1 persistence in latent reservoirs remains a major obstacle to a cure. We postulate that HIV-1 silencing factors suppress HIV-1 reactivation and that inhibition of these factors will increase HIV-1 reactivation. To identify HIV-1 silencing factors, we conducted a genome-wide CRISPR inhibition (CRISPRi) screen using four CRISPRi-ready, HIV-1-d6-GFP-infected Jurkat T cell clones with distinct integration sites. We sorted cells with increased green fluorescent protein (GFP) expression and captured single guide RNAs (sgRNAs) via targeted deep sequencing. We identified 18 HIV-1 silencing factors that were significantly enriched in HIV-1-d6-GFPhigh cells. Among them, SLTM (scaffold attachment factor B-like transcription modulator) is an epigenetic and transcriptional modulator having both DNA and RNA binding capacities not previously known to affect HIV-1 transcription. Knocking down SLTM by CRISPRi significantly increased HIV-1-d6-GFP expression (by 1.9- to 4.2-fold) in three HIV-1-d6-GFP-Jurkat T cell clones. Furthermore, SLTM knockdown increased the chromatin accessibility of HIV-1 and the gene in which HIV-1 is integrated but not the housekeeping gene POLR2A. To test whether SLTM inhibition can reactivate HIV-1 and further induce cell death of HIV-1-infected cells ex vivo, we established a small interfering RNA (siRNA) knockdown method that reduced SLTM expression in CD4+ T cells from 10 antiretroviral therapy (ART)-treated, virally suppressed, HIV-1-infected individuals ex vivo. Using limiting dilution culture, we found that SLTM knockdown significantly reduced the frequency of HIV-1-infected cells harboring inducible HIV-1 by 62.2% (0.56/106 versus 1.48/106 CD4+ T cells [P = 0.029]). Overall, our study indicates that SLTM inhibition reactivates HIV-1 in vitro and induces cell death of HIV-1-infected cells ex vivo. Our study identified SLTM as a novel therapeutic target. IMPORTANCE HIV-1-infected cells, which can survive drug treatment and immune cell killing, prevent an HIV-1 cure. Immune recognition of infected cells requires HIV-1 protein expression; however, HIV-1 protein expression is limited in infected cells after long-term therapy. The ways in which the HIV-1 provirus is blocked from producing protein are unknown. We identified a new host protein that regulates HIV-1 gene expression. We also provided a new method of studying HIV-1-host factor interactions in cells from infected individuals. These improvements may enable future strategies to reactivate HIV-1 in infected individuals so that infected cells can be killed by immune cells, drug treatment, or the virus itself.

A comparison of experimental assays and analytical methods for genome-wide identification of active enhancers

Mounting evidence supports the idea that transcriptional patterns serve as more specific identifiers of active enhancers than histone marks; however, the optimal strategy to identify active enhancers both experimentally and computationally has not been determined. Here, we compared 13 genome-wide RNA sequencing (RNA-seq) assays in K562 cells and show that nuclear run-on followed by cap-selection assay (GRO/PRO-cap) has advantages in enhancer RNA detection and active enhancer identification. We also introduce a tool, peak identifier for nascent transcript starts (PINTS), to identify active promoters and enhancers genome wide and pinpoint the precise location of 5' transcription start sites. Finally, we compiled a comprehensive enhancer candidate compendium based on the detected enhancer RNA (eRNA) transcription start sites (TSSs) available in 120 cell and tissue types, which can be accessed at https://pints.yulab.org . With knowledge of the best available assays and pipelines, this large-scale annotation of candidate enhancers will pave the way for selection and characterization of their functions in a time- and labor-efficient manner.

CRISPR screening in cancer stem cells

Cancer stem cells (CSCs) are a subpopulation of tumor cells with self-renewal ability. Increasing evidence points to the critical roles of CSCs in tumorigenesis, metastasis, therapy resistance, and cancer relapse. As such, the elimination of CSCs improves cancer treatment outcomes. However, challenges remain due to limited understanding of the molecular mechanisms governing self-renewal and survival of CSCs. Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 screening has been increasingly used to identify genetic determinants in cancers. In this primer, we discuss the progress made and emerging opportunities of coupling advanced CRISPR screening systems with CSC models to reveal the understudied vulnerabilities of CSCs.

Emerging technologies for prenatal diagnosis: The application of whole genome and RNA sequencing

DNA sequencing technologies for clinical genetic testing have been rapidly evolving in recent years, and steadily become more important within the field of prenatal diagnostics. This review aims to give an overview of recent developments and to describe how they have the potential to fill the gaps of the currently clinically implemented methods for prenatal diagnosis of various genetic disorders. It has been shown for postnatal testing that whole genome sequencing provides a set of added benefits compared to exome sequencing, and it is to be expected that this will be the case for prenatal testing as well. RNA-sequencing, already used postnatally, can provide valuable complementary data to DNA-based testing, and aid in variant interpretation. While not ready for clinical implementation, emerging technologies such as long-read and Hi-C sequencing analyses might add to the toolbox for interpreting the expanding genetic data sets generated by genome-wide sequencing. Lastly, we also discuss some more practical implications of introducing these emerging technologies, which generate larger and larger genomic data sets, in the prenatal field.

CRISPRi for specific inhibition of miRNA clusters and miRNAs with high sequence homology

miRNAs form a class of noncoding RNAs, involved in post-transcriptional regulation of gene expression, broadly studied for their involvement in physiological and pathological context. Inhibition of mature miRNA transcripts, commonly used in miRNA loss-of-function experiments, may not be specific in case of miRNAs with high sequence homology, e.g. miRNAs from the same seed family. Phenotypic effects of miRNA repression might be biased by the repression of highly similar miRNAs. Another challenge is simultaneous inhibition of multiple miRNAs encoded within policistronic clusters, potentially co-regulating common biological processes. To elucidate roles of miRNA clusters and miRNAs with high sequence homology, it is of key importance to selectively repress only the miRNAs of interest. Targeting miRNAs on genomic level with CRISPR/dCas9-based methods is an attractive alternative to blocking mature miRNAs. Yet, so far no clear guidelines on the design of CRISPR inhibition (CRISPRi) experiments, specifically for miRNA repression, have been proposed. To address this need, here we propose a strategy for effective inhibition of miRNAs and miRNA clusters using CRISPRi. We provide clues on how to approach the challenges in using CRISPR/dCas in miRNA studies, which include prediction of miRNA transcription start sites (TSSs) and the design of single guide RNAs (sgRNAs). The strategy implements three TSS prediction online tools, dedicated specifically for miRNAs: miRStart, FANTOM 5 miRNA atlas, DIANA-miRGen, and CRISPOR tool for sgRNAs design; it includes testing and selection of optimal sgRNAs. We demonstrate that compared to siRNA/shRNA-based miRNA silencing, CRISPRi improves the repression specificity for miRNAs with highly similar sequence and contribute to higher uniformity of the effects of silencing the whole miRNA clusters. This strategy may be adapted for CRISPR-mediated activation (CRISPRa) of miRNA expression.

Enhancer RNA Transcription Is Essential for a Novel CSF1 Enhancer in Triple-Negative Breast Cancer

Enhancers are critical regulatory elements in the genome that help orchestrate spatiotemporal patterns of gene expression during development and normal physiology. In cancer, enhancers are often rewired by various genetic and epigenetic mechanisms for the activation of oncogenes that lead to initiation and progression. A key feature of active enhancers is the production of non-coding RNA molecules called enhancer RNAs, whose functions remain unknown but can be used to specify active enhancers de novo. Using a combination of eRNA transcription and chromatin modifications, we have identified a novel enhancer located 30 kb upstream of Colony Stimulating Factor 1 (CSF1). Notably, CSF1 is implicated in the progression of breast cancer, is overexpressed in triple-negative breast cancer (TNBC) cell lines, and its enhancer is primarily active in TNBC patient tumors. Genomic deletion of the enhancer (via CRISPR/Cas9) enabled us to validate this regulatory element as a bona fide enhancer of CSF1 and subsequent cell-based assays revealed profound effects on cancer cell proliferation, colony formation, and migration. Epigenetic silencing of the enhancer via CRISPR-interference assays (dCas9-KRAB) coupled to RNA-sequencing, enabled unbiased identification of additional target genes, such as RSAD2, that are predictive of clinical outcome. Additionally, we repurposed the RNA-guided RNA-targeting CRISPR-Cas13 machinery to specifically degrade the eRNAs transcripts produced at this enhancer to determine the consequences on CSF1 mRNA expression, suggesting a post-transcriptional role for these non-coding transcripts. Finally, we test our eRNA-dependent model of CSF1 enhancer function and demonstrate that our results are extensible to other forms of cancer. Collectively, this work describes a novel enhancer that is active in the TNBC subtype, which is associated with cellular growth, and requires eRNA transcripts for proper enhancer function. These results demonstrate the significant impact of enhancers in cancer biology and highlight their potential as tractable targets for therapeutic intervention.

Shear stress switches the association of endothelial enhancers from ETV/ETS to KLF transcription factor binding sites

Endothelial cells (ECs) lining blood vessels are exposed to mechanical forces, such as shear stress. These forces control many aspects of EC biology, including vascular tone, cell migration and proliferation. Despite a good understanding of the genes responding to shear stress, our insight into the transcriptional regulation of these genes is much more limited. Here, we set out to study alterations in the chromatin landscape of human umbilical vein endothelial cells (HUVEC) exposed to laminar shear stress. To do so, we performed ChIP-Seq for H3K27 acetylation, indicative of active enhancer elements and ATAC-Seq to mark regions of open chromatin in addition to RNA-Seq on HUVEC exposed to 6 h of laminar shear stress. Our results show a correlation of gained and lost enhancers with up and downregulated genes, respectively. DNA motif analysis revealed an over-representation of KLF transcription factor (TF) binding sites in gained enhancers, while lost enhancers contained more ETV/ETS motifs. We validated a subset of flow responsive enhancers using luciferase-based reporter constructs and CRISPR-Cas9 mediated genome editing. Lastly, we characterized the shear stress response in ECs of zebrafish embryos using RNA-Seq. Our results lay the groundwork for the exploration of shear stress responsive elements in controlling EC biology.

Androgen Receptor-Mediated Transcription in Prostate Cancer

Androgen receptor (AR)-mediated transcription is critical in almost all stages of prostate cancer (PCa) growth and differentiation. This process involves a complex interplay of coregulatory proteins, chromatin remodeling complexes, and other transcription factors that work with AR at cis-regulatory enhancer regions to induce the spatiotemporal transcription of target genes. This enhancer-driven mechanism is remarkably dynamic and undergoes significant alterations during PCa progression. In this review, we discuss the AR mechanism of action in PCa with a focus on how cis-regulatory elements modulate gene expression. We explore emerging evidence of genetic variants that can impact AR regulatory regions and alter gene transcription in PCa. Finally, we highlight several outstanding questions and discuss potential mechanisms of this critical transcription factor.

Epigenome rewiring in human pluripotent stem cells

The epigenome plays a crucial role in modulating the activity of regulatory elements, thereby orchestrating diverse transcriptional programs during embryonic development. Human (h)PSC stepwise differentiation provides an excellent platform for capturing dynamic epigenomic events during lineage transition in human development. Here we discuss how recent technological advances, from epigenomic mapping to targeted perturbation, are providing a more comprehensive appreciation of remodeling of the chromatin landscape during human development with implications for aberrant rewiring in disease. We predict that the continuous innovation of hPSC differentiation methods, epigenome mapping, and CRISPR (clustered regularly interspaced short palindromic repeats) perturbation technologies will allow researchers to build toward not only a comprehensive understanding of the epigenomic mechanisms governing development, but also a highly flexible way to model diseases with opportunities for translation.

Re-building limbs, one cell at a time

New techniques for visualizing and interrogating single cells hold the key to unlocking the underlying mechanisms of salamander limb regeneration.

Computational identification of clonal cells in single-cell CRISPR screens

BACKGROUND

Single-cell CRISPR screens are powerful tools to understand genome function by linking genetic perturbations to transcriptome-wide phenotypes. However, since few cells can be affordably sequenced in these screens, biased sampling of cells could affect data interpretation. One potential source of biased sampling is clonal cell expansion.

RESULTS

Here, we identify clonal cells in single cell screens using multiplexed sgRNAs as barcodes. We find that the cells in each clone share transcriptional similarities and bear segmental copy number changes. These analyses suggest that clones are genetically distinct. Finally, we show that the transcriptional similarities of clonally expanded cells contribute to false positives in single-cell CRISPR screens.

CONCLUSIONS

Experimental conditions that reduce clonal expansion or computational filtering of clonal cells will improve the reliability of single-cell CRISPR screens.

CRISPR/ Cas9 Off-targets: Computational Analysis of Causes, Prediction, Detection, and Overcoming Strategies

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CRISPR/Cas9 technology is a highly flexible RNA-guided endonuclease (RGEN) based gene-editing tool that has transformed the field of genomics, gene therapy, and genome/ epigenome imaging. Its wide range of applications provides immense scope for understanding as well as manipulating genetic/epigenetic elements. However, the RGEN is prone to off-target mutagenesis that leads to deleterious effects. This review details the molecular and cellular mechanisms underlying the off-target activity, various available detection tools and prediction methodology ranging from sequencing to machine learning approaches, and the strategies to overcome/minimise off-targets. A coherent and concise method increasing target precision would prove indispensable to concrete manipulation and interpretation of genome editing results that can revolutionise therapeutics, including clarity in genome regulatory mechanisms during development.

New horizons in the stormy sea of multimodal single-cell data integration

While measurements of RNA expression have dominated the world of single-cell analyses, new single-cell techniques increasingly allow collection of different data modalities, measuring different molecules, structural connections, and intermolecular interactions. Integrating the resulting multimodal single-cell datasets is a new bioinformatics challenge. Equally important, it is a new experimental design challenge for the bench scientist, who is not only choosing from a myriad of techniques for each data modality but also faces new challenges in experimental design. The ultimate goal is to design, execute, and analyze multimodal single-cell experiments that are more than just descriptive but enable the learning of new causal and mechanistic biology. This objective requires strict consideration of the goals behind the analysis, which might range from mapping the heterogeneity of a cellular population to assembling system-wide causal networks that can further our understanding of cellular functions and eventually lead to models of tissues and organs. We review steps and challenges toward this goal. Single-cell transcriptomics is now a mature technology, and methods to measure proteins, lipids, small-molecule metabolites, and other molecular phenotypes at the single-cell level are rapidly developing. Integrating these single-cell readouts so that each cell has measurements of multiple types of data, e.g., transcriptomes, proteomes, and metabolomes, is expected to allow identification of highly specific cellular subpopulations and to provide the basis for inferring causal biological mechanisms.

Single-Cell Analysis of the Transcriptome and Epigenome

Epigenome regulation has emerged as an important mechanism for the maintenance of organ function in health and disease. Dissecting epigenomic alterations and resultant gene expression changes in single cells provides unprecedented resolution and insight into cellular diversity, modes of gene regulation, transcription factor dynamics and 3D genome organization. In this chapter, we summarize the transformative single-cell epigenomic technologies that have deepened our understanding of the fundamental principles of gene regulation. We provide a historical perspective of these methods, brief procedural outline with emphasis on the computational tools used to meaningfully dissect information. Our overall goal is to aid scientists using these technologies in their favorite system of interest.

Immune cell profiling in atherosclerosis: role in research and precision medicine

Inflammation is intimately involved at all stages of atherosclerosis and remains a substantial residual cardiovascular risk factor in optimally treated patients. The proof of concept that targeting inflammation reduces cardiovascular events in patients with a history of myocardial infarction has highlighted the urgent need to identify new immunotherapies to treat patients with atherosclerotic cardiovascular disease. Importantly, emerging data from new clinical trials show that successful immunotherapies for atherosclerosis need to be tailored to the specific immune alterations in distinct groups of patients. In this Review, we discuss how single-cell technologies - such as single-cell mass cytometry, single-cell RNA sequencing and cellular indexing of transcriptomes and epitopes by sequencing - are ideal for mapping the cellular and molecular composition of human atherosclerotic plaques and how these data can aid in the discovery of new precise immunotherapies. We also argue that single-cell data from studies in humans need to be rigorously validated in relevant experimental models, including rapidly emerging single-cell CRISPR screening technologies and mouse models of atherosclerosis. Finally, we discuss the importance of implementing single-cell immune monitoring tools in early phases of drug development to aid in the precise selection of the target patient population for data-driven translation into randomized clinical trials and the successful translation of new immunotherapies into the clinic.

SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis

Single-cell CRISPR screens are a promising biotechnology for mapping regulatory elements to target genes at genome-wide scale. However, technical factors like sequencing depth impact not only expression measurement but also perturbation detection, creating a confounding effect. We demonstrate on two single-cell CRISPR screens how these challenges cause calibration issues. We propose SCEPTRE: analysis of single-cell perturbation screens via conditional resampling, which infers associations between perturbations and expression by resampling the former according to a working model for perturbation detection probability in each cell. SCEPTRE demonstrates very good calibration and sensitivity on CRISPR screen data, yielding hundreds of new regulatory relationships supported by orthogonal biological evidence.

CRISPR-Based Approaches for the High-Throughput Characterization of Long Non-Coding RNAs

Over the last decade, tens of thousands of new long non-coding RNAs (lncRNAs) have been identified in the human genome. Nevertheless, except for a handful of genes, the genetic characteristics and functions of most of these lncRNAs remain elusive; this is partially due to their relatively low expression, high tissue specificity, and low conservation across species. A major limitation for determining the function of lncRNAs was the lack of methodologies suitable for studying these genes. The recent development of CRISPR/Cas9 technology has opened unprecedented opportunities to uncover the genetic and functional characteristics of the non-coding genome via targeted and high-throughput approaches. Specific CRISPR/Cas9-based approaches were developed to target lncRNA loci. Some of these approaches involve modifying the sequence, but others were developed to study lncRNAs by inducing transcriptional and epigenetic changes. The discovery of other programable Cas proteins broaden our possibilities to target RNA molecules with greater precision and accuracy. These approaches allow for the knock-down and characterization of lncRNAs. Here, we review how various CRISPR-based strategies have been used to characterize lncRNAs with important functions in different biological contexts and how these approaches can be further utilized to improve our understanding of the non-coding genome.

A multi-omic single-cell landscape of human gynecologic malignancies

Deconvolution of regulatory mechanisms that drive transcriptional programs in cancer cells is key to understanding tumor biology. Herein, we present matched transcriptome (scRNA-seq) and chromatin accessibility (scATAC-seq) profiles at single-cell resolution from human ovarian and endometrial tumors processed immediately following surgical resection. This dataset reveals the complex cellular heterogeneity of these tumors and enabled us to quantitatively link variation in chromatin accessibility to gene expression. We show that malignant cells acquire previously unannotated regulatory elements to drive hallmark cancer pathways. Moreover, malignant cells from within the same patients show substantial variation in chromatin accessibility linked to transcriptional output, highlighting the importance of intratumoral heterogeneity. Finally, we infer the malignant cell type-specific activity of transcription factors. By defining the regulatory logic of cancer cells, this work reveals an important reliance on oncogenic regulatory elements and highlights the ability of matched scRNA-seq/scATAC-seq to uncover clinically relevant mechanisms of tumorigenesis in gynecologic cancers.

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.

Transcriptional enhancers and their communication with gene promoters

Transcriptional enhancers play a key role in the initiation and maintenance of gene expression programmes, particularly in metazoa. How these elements control their target genes in the right place and time is one of the most pertinent questions in functional genomics, with wide implications for most areas of biology. Here, we synthesise classic and recent evidence on the regulatory logic of enhancers, including the principles of enhancer organisation, factors that facilitate and delimit enhancer-promoter communication, and the joint effects of multiple enhancers. We show how modern approaches building on classic insights have begun to unravel the complexity of enhancer-promoter relationships, paving the way towards a quantitative understanding of gene control.

A new era in functional genomics screens

The past 25 years of genomics research first revealed which genes are encoded by the human genome and then a detailed catalogue of human genome variation associated with many diseases. Despite this, the function of many genes and gene regulatory elements remains poorly characterized, which limits our ability to apply these insights to human disease. The advent of new CRISPR functional genomics tools allows for scalable and multiplexable characterization of genes and gene regulatory elements encoded by the human genome. These approaches promise to reveal mechanisms of gene function and regulation, and to enable exploration of how genes work together to modulate complex phenotypes.

Parallel characterization of cis-regulatory elements for multiple genes using CRISPRpath

Current pooled CRISPR screens for cis-regulatory elements (CREs), based on transcriptional output changes, are typically limited to characterizing CREs of only one gene. Here, we describe CRISPRpath, a scalable screening strategy for parallelly characterizing CREs of genes linked to the same biological pathway and converging phenotypes. We demonstrate the ability of CRISPRpath for simultaneously identifying functional enhancers of six genes in the 6-thioguanine–induced DNA mismatch repair pathway using both CRISPR interference (CRISPRi) and CRISPR nuclease (CRISPRn) approaches. Sixty percent of the identified enhancers are known promoters with distinct epigenomic features compared to other active promoters, including increased chromatin accessibility and interactivity. Furthermore, by imposing different levels of selection pressure, CRISPRpath can distinguish enhancers exerting strong impact on gene expression from those exerting weak impact. Our results offer a nuanced view of cis-regulation and demonstrate that CRISPRpath can be leveraged for understanding the complex gene regulatory program beyond transcriptional output at scale.

Non-coding driver mutations in human cancer

Tumour formation involves random mutagenic events and positive evolutionary selection acting on a subset of such events, referred to as driver mutations. A decade of careful surveying of tumour DNA using exome-based analyses has revealed a multitude of protein-coding somatic driver mutations, some of which are clinically actionable. Today, a transition towards whole-genome analysis is well under way, technically enabling the discovery of potential driver mutations occurring outside protein-coding sequences. Mutations are abundant in this vast non-coding space, which is more than 50 times larger than the coding exome, but reliable identification of selection signals in non-coding DNA remains a challenge. In this Review, we discuss recent findings in the field, where the emerging landscape is one in which non-coding driver mutations appear to be relatively infrequent. Nevertheless, we highlight several notable discoveries. We consider possible reasons for the relative absence of non-coding driver events, as well as the difficulties associated with detecting signals of positive selection in non-coding DNA.

Integrating Biomaterials and Genome Editing Approaches to Advance Biomedical Science

The recent discovery and subsequent development of the CRISPR-Cas9 (clustered regularly interspaced short palindromic repeat-CRISPR-associated protein 9) platform as a precise genome editing tool have transformed biomedicine. As these CRISPR-based tools have matured, multiple stages of the gene editing process and the bioengineering of human cells and tissues have advanced. Here, we highlight recent intersections in the development of biomaterials and genome editing technologies. These intersections include the delivery of macromolecules, where biomaterial platforms have been harnessed to enable nonviral delivery of genome engineering tools to cells and tissues in vivo. Further, engineering native-like biomaterial platforms for cell culture facilitates complex modeling of human development and disease when combined with genome engineering tools. Deeper integration of biomaterial platforms in these fields could play a significant role in enabling new breakthroughs in the application of gene editing for the treatment of human disease.

Exploiting Single-Cell Tools in Gene and Cell Therapy

Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.

Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens

Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.

Functional genomics of autoimmune diseases

For more than a decade, genome-wide association studies have been applied to autoimmune diseases and have expanded our understanding on the pathogeneses. Genetic risk factors associated with diseases and traits are essentially causative. However, elucidation of the biological mechanism of disease from genetic factors is challenging. In fact, it is difficult to identify the causal variant among multiple variants located on the same haplotype or linkage disequilibrium block and thus the responsible biological genes remain elusive. Recently, multiple studies have revealed that the majority of risk variants locate in the non-coding region of the genome and they are the most likely to regulate gene expression such as quantitative trait loci. Enhancer, promoter and long non-coding RNA appear to be the main target mechanisms of the risk variants. In this review, we discuss functional genetics to challenge these puzzles.

Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks

Algorithms that accurately predict gene structure from primary sequence alone were transformative for annotating the human genome. Can we also predict the expression levels of genes based solely on genome sequence? Here, we sought to apply deep convolutional neural networks toward that goal. Surprisingly, a model that includes only promoter sequences and features associated with mRNA stability explains 59% and 71% of variation in steady-state mRNA levels in human and mouse, respectively. This model, termed Xpresso, more than doubles the accuracy of alternative sequence-based models and isolates rules as predictive as models relying on chromatic immunoprecipitation sequencing (ChIP-seq) data. Xpresso recapitulates genome-wide patterns of transcriptional activity, and its residuals can be used to quantify the influence of enhancers, heterochromatic domains, and microRNAs. Model interpretation reveals that promoter-proximal CpG dinucleotides strongly predict transcriptional activity. Looking forward, we propose cell-type-specific gene-expression predictions based solely on primary sequences as a grand challenge for the field.

FBA: feature barcoding analysis for single cell RNA-Seq

MOTIVATION

Single cell RNA-Seq (scRNA-Seq) has broadened our understanding of cellular heterogeneity and provided valuable insights into cellular functions. Recent experimental strategies extend scRNA-Seq readouts to include additional features, including cell surface proteins and genomic perturbations. These "feature barcoding" strategies rely on converting molecular and cellular features to unique sequence barcodes, which are then detected with the transcriptome.

RESULTS

Here, we introduce FBA, a flexible and streamlined package to perform quality control, quantification, demultiplexing, multiplet detection, clustering, and visualization of feature barcoding assays.

AVAILABILITY

FBA is available on PyPi at https://pypi.org/project/fba and on GitHub at https://github.com/jlduan/fba.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Super-Enhancer Redistribution as a Mechanism of Broad Gene Dysregulation in Repeatedly Drug-Treated Cancer Cells

Cisplatin is an antineoplastic drug administered at suboptimal and intermittent doses to avoid life-threatening effects. Although this regimen shortly improves symptoms in the short term, it also leads to more malignant disease in the long term. We describe a multilayered analysis ranging from chromatin to translation-integrating chromatin immunoprecipitation sequencing (ChIP-seq), global run-on sequencing (GRO-seq), RNA sequencing (RNA-seq), and ribosome profiling-to understand how cisplatin confers (pre)malignant features by using a well-established ovarian cancer model of cisplatin exposure. This approach allows us to segregate the human transcriptome into gene modules representing distinct regulatory principles and to characterize that the most cisplatin-disrupted modules are associated with underlying events of super-enhancer plasticity. These events arise when cancer cells initiate without ultimately ending the program of drug-stimulated death. Using a PageRank-based algorithm, we predict super-enhancer regulator ISL1 as a driver of this plasticity and validate this prediction by using CRISPR/dCas9-KRAB inhibition (CRISPRi) and CRISPR/dCas9-VP64 activation (CRISPRa) tools. Together, we propose that cisplatin reprograms cancer cells when inducing them to undergo near-to-death experiences.

Using CRISPR to understand and manipulate gene regulation

Understanding how genes are expressed in the correct cell types and at the correct level is a key goal of developmental biology research. Gene regulation has traditionally been approached largely through observational methods, whereas perturbational approaches have lacked precision. CRISPR-Cas9 has begun to transform the study of gene regulation, allowing for precise manipulation of genomic sequences, epigenetic functionalization and gene expression. CRISPR-Cas9 technology has already led to the discovery of new paradigms in gene regulation and, as new CRISPR-based tools and methods continue to be developed, promises to transform our knowledge of the gene regulatory code and our ability to manipulate cell fate. Here, we discuss the current and future application of the emerging CRISPR toolbox toward predicting gene regulatory network behavior, improving stem cell disease modeling, dissecting the epigenetic code, reprogramming cell fate and treating diseases of gene dysregulation.

Defining super-enhancer landscape in triple-negative breast cancer by multiomic profiling

Breast cancer is a heterogeneous disease, affecting over 3.5 million women worldwide, yet the functional role of cis-regulatory elements including super-enhancers in different breast cancer subtypes remains poorly characterized. Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with a poor prognosis. Here we apply integrated epigenomic and transcriptomic profiling to uncover super-enhancer heterogeneity between breast cancer subtypes, and provide clinically relevant biological insights towards TNBC. Using CRISPR/Cas9-mediated gene editing, we identify genes that are specifically regulated by TNBC-specific super-enhancers, including FOXC1 and MET, thereby unveiling a mechanism for specific overexpression of the key oncogenes in TNBC. We also identify ANLN as a TNBC-specific gene regulated by super-enhancer. Our studies reveal a TNBC-specific epigenomic landscape, contributing to the dysregulated oncogene expression in breast tumorigenesis.

Single-Cell Toolkits Opening a New Era for Cell Engineering

Since the introduction of RNA sequencing (RNA-seq) as a high-throughput mRNA expression analysis tool, this procedure has been increasingly implemented to identify cell-level transcriptome changes in a myriad of model systems. However, early methods processed cell samples in bulk, and therefore the unique transcriptomic patterns of individual cells would be lost due to data averaging. Nonetheless, the recent and continuous development of new single-cell RNA sequencing (scRNA-seq) toolkits has enabled researchers to compare transcriptomes at a single-cell resolution, thus facilitating the analysis of individual cellular features and a deeper understanding of cellular functions. Nonetheless, the rapid evolution of high throughput single-cell "omics" tools has created the need for effective hypothesis verification strategies. Particularly, this issue could be addressed by coupling cell engineering techniques with single-cell sequencing. This approach has been successfully employed to gain further insights into disease pathogenesis and the dynamics of differentiation trajectories. Therefore, this review will discuss the current status of cell engineering toolkits and their contributions to single-cell and genome-wide data collection and analyses.

Interrogating immune cells and cancer with CRISPR-Cas9

CRISPR-Cas9 technologies have transformed the study of genetic pathways governing cellular differentiation and function. Recent advances have adapted these methods to immune cells, which has accelerated the pace of functional genomics in immunology and enabled new avenues for the design of cellular immunotherapies for cancer. In this review, we summarize recent developments in CRISPR-Cas9 technology and discuss how they have been leveraged to discover and manipulate novel genetic regulators of the immune system. We envision that these results will provide a valuable resource to aid in the design, implementation, and interpretation of CRISPR-Cas9-based screens in immunology and immuno-oncology.

Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

Genome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

Detection of gene cis-regulatory element perturbations in single-cell transcriptomes

We introduce poly-adenine CRISPR gRNA-based single-cell RNA-sequencing (pAC-Seq), a method that enables the direct observation of guide RNAs (gRNAs) in scRNA-seq. We use pAC-Seq to assess the phenotypic consequences of CRISPR/Cas9 based alterations of gene cis-regulatory regions. We show that pAC-Seq is able to detect cis-regulatory-induced alteration of target gene expression even when biallelic loss of target gene expression occurs in only ~5% of cells. This low rate of biallelic loss significantly increases the number of cells required to detect the consequences of changes to the regulatory genome, but can be ameliorated by transcript-targeted sequencing. Based on our experimental results we model the power to detect regulatory genome induced transcriptomic effects based on the rate of mono/biallelic loss, baseline gene expression, and the number of cells per target gRNA.

Loop competition and extrusion model predicts CTCF interaction specificity

Three-dimensional chromatin looping interactions play an important role in constraining enhancer–promoter interactions and mediating transcriptional gene regulation. CTCF is thought to play a critical role in the formation of these loops, but the specificity of which CTCF binding events form loops and which do not is difficult to predict. Loops often have convergent CTCF binding site motif orientation, but this constraint alone is only weakly predictive of genome-wide interaction data. Here we present an easily interpretable and simple mathematical model of CTCF mediated loop formation which is consistent with Cohesin extrusion and can predict ChIA-PET CTCF looping interaction measurements with high accuracy. Competition between overlapping loops is a critical determinant of loop specificity. We show that this model is consistent with observed chromatin interaction frequency changes induced by CTCF binding site deletion, inversion, and mutation, and is also consistent with observed constraints on validated enhancer–promoter interactions.

Boundaries of topologically associated domains in genomes are marked by CTCF and cohesin binding. Here the authors predict CTCF interaction specificity by building a simple mathematical model with features including loop competition and extrusion.

Mind the translational gap: using iPS cell models to bridge from genetic discoveries to perturbed pathways and therapeutic targets

Autism spectrum disorder (ASD) comprises a group of neurodevelopmental disorders characterized by impaired social interactions as well as the presentation of restrictive and repetitive behaviors. ASD is highly heritable but genetically heterogenous with both common and rare genetic variants collaborating to predispose individuals to the disorder. In this review, we synthesize recent efforts to develop human induced pluripotent stem cell (iPSC)-derived models of ASD-related phenotypes. We firstly address concerns regarding the relevance and validity of available neuronal iPSC-derived models. We then critically evaluate the robustness of various differentiation and cell culture protocols used for producing cell types of relevance to ASD. By exploring iPSC models of ASD reported thus far, we examine to what extent cellular and neuronal phenotypes with potential relevance to ASD can be linked to genetic variants found to underlie it. Lastly, we outline promising strategies by which iPSC technology can both enhance the power of genetic studies to identify ASD risk factors and nominate pathways that are disrupted across groups of ASD patients that might serve as common points for therapeutic intervention.

Super enhancers-Functional cores under the 3D genome

Complex biochemical reactions take place in the nucleus all the time. Transcription machines must follow the rules. The chromatin state, especially the three-dimensional structure of the genome, plays an important role in gene regulation and expression. The super enhancers are important for defining cell identity in mammalian developmental processes and human diseases. It has been shown that the major components of transcriptional activation complexes are recruited by super enhancer to form phase-separated condensates. We summarize the current knowledge about super enhancer in the 3D genome. Furthermore, a new related transcriptional regulation model from super enhancer is outlined to explain its role in the mammalian cell progress.

Genome editing of immune cells using CRISPR/Cas9

The ability to read, write, and edit genomic information in living organisms can have a profound impact on research, health, economic, and environmental issues. The CRISPR/Cas system, recently discovered as an adaptive immune system in prokaryotes, has revolutionized the ease and throughput of genome editing in mammalian cells and has proved itself indispensable to the engineering of immune cells and identification of novel immune mechanisms. In this review, we summarize the CRISPR/Cas9 system and the history of its discovery and optimization. We then focus on engineering T cells and other types of immune cells, with emphasis on therapeutic applications. Last, we describe the different modifications of Cas9 and their recent applications in the genome-wide screening of immune cells.

Epigenome engineering: new technologies for precision medicine

Chromatin adopts different configurations that are regulated by reversible covalent modifications, referred to as epigenetic marks. Epigenetic inhibitors have been approved for clinical use to restore epigenetic aberrations that result in silencing of tumor-suppressor genes, oncogene addictions, and enhancement of immune responses. However, these drugs suffer from major limitations, such as a lack of locus selectivity and potential toxicities. Technological advances have opened a new era of precision molecular medicine to reprogram cellular physiology. The locus-specificity of CRISPR/dCas9/12a to manipulate the epigenome is rapidly becoming a highly promising strategy for personalized medicine. This review focuses on new state-of-the-art epigenome editing approaches to modify the epigenome of neoplasms and other disease models towards a more 'normal-like state', having characteristics of normal tissue counterparts. We highlight biomolecular engineering methodologies to assemble, regulate, and deliver multiple epigenetic effectors that maximize the longevity of the therapeutic effect, and we discuss limitations of the platforms such as targeting efficiency and intracellular delivery for future clinical applications.

PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids

Crucial decisions involving cell fate and connectivity that shape the distinctive development of the human brain occur in the embryonic and fetal stages-stages that are difficult to access and investigate in humans. The last decade has seen an impressive increase in resources-from atlases and databases to biological models-that is progressively lifting the curtain on this critical period. In this review, we describe the current state of genomic, transcriptomic, and epigenomic datasets charting the development of normal human brain with a particular focus on recent single-cell technologies. We discuss the emergence of brain organoids generated from pluripotent stem cells as a model to compensate for the limited availability of fetal tissue. Indeed, comparisons of neural lineages, transcriptional dynamics, and noncoding element activity between fetal brain and organoids have helped identify gene regulatory networks functioning at early stages of brain development. Altogether, we argue that large multi-omics investigations have pushed brain development into the "big data" era, and that current and future transversal approaches needed to leverage both fetal brain and organoid resources promise to answer major questions of brain biology and psychiatry.

A primer on single-cell genomics in myeloid biology

PURPOSE OF REVIEW

Understanding the fast-moving field of single-cell technologies, as applied to myeloid biology, requires an appreciation of basic molecular, informatics, and biological concepts. Here, we highlight both key and recent articles to illustrate basic concepts for those new to molecular single-cell analyses in myeloid hematology.

RECENT FINDINGS

Recent studies apply single-cell omics to discover novel cell populations, construct relationships between cell populations, reconfigure the organization of hematopoiesis, and study hematopoietic lineage tree and fate choices. Accompanying development of technologies, new informatic tools have emerged, providing exciting new insights.

SUMMARY

Hematopoietic stem and progenitor cells are regulated by complex intrinsic and extrinsic factors to produce blood cell types. In this review, we discuss recent advances in single-cell omics to profile these cells, methods to infer cell type identify, and trajectories from molecular omics data to ultimately derive new insights into hematopoietic stem and progenitor cell biology. We further discuss future applications of these technologies to understand hematopoietic cell interactions, function, and development. The goal is to offer a comprehensive overview of current single-cell technologies and their impact on our understanding of myeloid cell development for those new to single-cell analyses.

CRISPR technologies for precise epigenome editing

The epigenome involves a complex set of cellular processes governing genomic activity. Dissecting this complexity necessitates the development of tools capable of specifically manipulating these processes. The repurposing of prokaryotic CRISPR systems has allowed for the development of diverse technologies for epigenome engineering. Here, we review the state of currently achievable epigenetic manipulations along with corresponding applications. With future optimization, CRISPR-based epigenomic editing stands as a set of powerful tools for understanding and controlling biological function.

Genome editing of immune cells using CRISPR/Cas9

The ability to read, write, and edit genomic information in living organisms can have a profound impact on research, health, economic, and environmental issues. The CRISPR/Cas system, recently discovered as an adaptive immune system in prokaryotes, has revolutionized the ease and throughput of genome editing in mammalian cells and has proved itself indispensable to the engineering of immune cells and identification of novel immune mechanisms. In this review, we summarize the CRISPR/ Cas9 system and the history of its discovery and optimization. We then focus on engineering T cells and other types of immune cells, with emphasis on therapeutic applications. Last, we describe the different modifications of Cas9 and their recent applications in the genome-wide screening of immune cells. [BMB Reports 2021; 54(1): 59-69].

HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes

Single-cell quantification of RNAs is important for understanding cellular heterogeneity and gene regulation, yet current approaches suffer from low sensitivity for individual transcripts, limiting their utility for many applications. Here we present Hybridization of Probes to RNA for sequencing (HyPR-seq), a method to sensitively quantify the expression of hundreds of chosen genes in single cells. HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and sequencing the amplicons to quantify the expression of chosen genes. HyPR-seq achieves high sensitivity for individual transcripts, detects nonpolyadenylated and low-abundance transcripts, and can profile more than 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell-type frequencies in tissue using low-abundance marker genes. By directing sequencing power to genes of interest and sensitively quantifying individual transcripts, HyPR-seq reduces costs by up to 100-fold compared to whole-transcriptome single-cell RNA-sequencing, making HyPR-seq a powerful method for targeted RNA profiling in single cells.

STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

STARR-seq technology has employed progressively more complex genomic libraries and increased sequencing depths. An issue with the increased complexity and depth is that the coverage in STARR-seq experiments is non-uniform, overdispersed, and often confounded by sequencing biases, such as GC content. Furthermore, STARR-seq readout is confounded by RNA secondary structure and thermodynamic stability. To address these potential confounders, we developed a negative binomial regression framework for uniformly processing STARR-seq data, called STARRPeaker. Moreover, to aid our effort, we generated whole-genome STARR-seq data from the HepG2 and K562 human cell lines and applied STARRPeaker to comprehensively and unbiasedly call enhancers in them.