Dear reader, I wrote this article to briefly introduce you to ten of the most influential methods that are reshaping biology research and biomedical applications. Each section includes a short description and notable uses of the respective method.

1. Integrative single-cell analysis
2. Microfluidic tools for early diagnostics
3. Next-generation CRISPR-Cas technologies
4. Genomically engineered organoid models
5. Biomaterials in stem cell research and therapy
6. Viral vector platforms for gene therapy
7. In vivo imaging for preclinical research and diagnostics
8. AI and deep learning in diagnostics and imaging
9. Nucleic acid-based nanotechnology
10. Modulating long non-coding RNAs for functional studies

Integrative Single-Cell Analysis

Simultaneous profiling of multiple types of molecules (DNA, RNA, protein) within a single cell (multimodal profiling) offers a highly comprehensive molecular view of individual cells. The most widely used single-cell sequencing approach is single-cell RNA sequencing (scRNA-seq), with a range of platforms for sensitive, highly multiplexed barcoded profiling 1,2,3. These advances were accompanied by an assortment of complementary single-cell genomic, epigenomic and proteomic profiling methods 4,5. Often, scRNA-seq is combined with the measurement of another cellular feature, such as DNA sequence, protein abundance, or epigenomic state. Alongside the promise of a more holistic representation of the cell state, multimodal profiling brings, however, significant computational challenges for integrating and learning from multiple types of data at single-cell resolution.

Applications of single-cell analysis include the following: (1) Lineage tracing of cellular phenotypes. The origins of cellular heterogeneity (for example, in a human tumor) can be understood by using single-cell proteomics and transcriptomics approaches 6. (2) Understanding cellular functionality. Flow cytometry cell sorting combined with single-cell proteomics has been employed in a study to characterize HSPCs (hematopoietic stem and progenitor cells) as early potent cytokine producers upon bacterial infection 7. (3) High-throughput drug screening. Cells can be separated in multi well plates, and cellular barcoding can be used to identify a given cell and its well location and the experimental conditions (specific drug dose) applied to that location. Many experimental conditions can then be simultaneously captured 8,9. (4) Understanding tumor heterogeneity. Single-cell DNA sequencing and RNA sequencing have been employed, for example, to demonstrate considerable DNA, transcript and protein heterogeneity among cells within a glioblastoma 10-13.

Microfluidic Tools For Early Diagnostics

Imaging tools such as X-ray, computed tomography, magnetic resonance imaging, or ultrasound are often used to detect solid tumors in cancer diagnosis. Identifying pathogens for monitoring and managing infectious diseases typically relies on agar plates, flow cytometry and PCR. These “classical” diagnostic tools, however, are costly, require complex facilities, have low sensitivity and long turnaround times. Miniaturized diagnostic tools have immense advantages in comparison, in terms of sample size, speed of analysis, portability, throughput, and multiplexing. Microfluidics platforms are now applied in sample preparation and delivery, in vitro assay development, sensors and detectors.

Applications of microfluidic tools include the following: (1) Detecting pathogens. The immunochromatographic strip is an example of a widely used microfluidic-based point-of-care (POC) platform that has found multiple applications, including in situ detection and rapid screening of bacterial, viral, and pathogenic antigens 14. (2) Detecting circulating tumor cells. Only a small fraction of about 0.02% of all CTCs is able to resist programmed cell death, escape the immune response, and ultimately grow into a metastatic lesion. This translates into several tens of CTCs among millions to billions of blood cells 15. First-generation CTC enrichment platforms utilizing antibodies for epithelial markers (as 90% of cancers develop from epithelial tissue) are limited by lack of robust CTC-specific biomarkers to isolate subpopulations of CTCs, are confounded by false positives and negatives, and CTC heterogeneity. Second-generation platforms based on label-free approaches are developed for accurate and high-throughput enrichment of CTCs. (3) Single-cell analysis. Single-cell sequencing and expression profiling platforms have been increasingly applied over the past decade to study mammalian organogenesis, embryo development, neuronal diversity, and intratumoral heterogeneity 16,17.

Next Generation Crispr-Cas Technologies

The development of CRISPR-Cas genome editing systems has revolutionized our ability to manipulate, detect and image specific DNA and RNA sequences in living cells of diverse species. Genome editing strategies can utilize non-homologous end joining (NHEJ) and homology-directed repair (HR) for DNA repair, as well as Cas9 fusions to single-base editing enzymes. In addition to targeting DNA, CRISPR-Cas tools adapted for binding or cleaving RNA have advanced RNA manipulation in living cells and are finding applications in research, medicine and diagnostics. Furthermore, by fusing nuclease-inactive and RNA-targeting Cas proteins to a wide array of effector proteins, we are gaining control over gene expression, epigenetic modifications and chromatin interactions.

Under a continual fast-pace advancement, CRISPR-Cas-based tools are rapidly progressing from basic biology research towards clinical use in gene and cell therapies. The ability to make precise, targeted genome modifications permits therapeutic strategies for a wide variety of diseases and disorders. Genome editing-based strategies are currently being explored, for example, for ocular diseases such as retinitis pigmentosa, which can result in blindness 18. Perhaps the most clinically advanced gene editing strategy is adoptive T cell immunotherapy. In this strategy, autologous T cells are engineered by insertion of transgenes encoding programmable chimeric antigen receptors into the endogenous T cell receptor a-constant gene, and then transferred back to the donor 19,20.

CRISPR-Cas Gene Editing Applications

Gene deletions. Following Cas9 cleavage, NHEJ-mediated DNA repair typically results in small insertions or deletions of nucleotides at repair sites, frame-shift mutations and premature stop codons (when a coding exon is targeted), which will disrupt gene expression. Cas9-mediated genomic deletions are accelerating the investigation of genes and genetic elements 21,22,23.

Gene insertions. By inserting a DNA sequence encoding an epitope or fluorescent tag into protein-coding genes, investigators can study the function of endogenously expressed proteins in native cellular settings. Flanked by gRNA-target sites, a tag can be released by Cas9 from a plasmid and inserted by NHEJ into a recipient genomic target site (also cleaved by Cas9) adjacent to the gene of interest. This system is called HITI, or homology-independent targeted integration, and its further development has made large-scale gene tagging possible 24-26.

Translocations. Following DNA double-strand breaks (DSBs), NHEJ can accidentally mediate joining of different chromosomes creating translocations. During cancer development, such translocations frequently create oncogenic fusion proteins. We can mimic and study the function of such cancer-relevant translocations in human cells through Cas9-mediated cleavage at two genomic loci 27-29.

Single-base editing. Point mutations are the most common genetic variants associated with human diseases. Creating genetic disease models by editing single nucleotide bases is essential for the development of corrective therapeutics. For directed single base editing, catalytically-deficient Cas9 has been fused to different editing enzymes, such as the cytidine deaminase APOBEC1 which directs C-to-T (or G-to-A) conversions 30,31.

High-throughput loss-of-function screens. Genomic knockout screens in mouse cells and in human cells have become possible due to the production of large gRNA libraries and the development of efficient lentiviral delivery platforms. Genetic interactions can also be dissected by multiplexed targeting 32,33.

CRISPR-Cas Gene Regulation Applications

CRISPR interference. Efficient gene repression can be achieved by tethering catalytically-deficient Cas9 (dCas9) to transcription repressor domains, such as KRAB (Krüppel associated box) 34. dCas9-KRAB targeted transcription repression is often accompanied by local heterochromatin formation. dCas9-KRAB is a robust tool for silencing gene expression in mammalian cels and can be directed against single genes or non-coding RNAs by targeting promoter regions, 5’ untranslated regions and proximal and distal enhancer elements.

CRISPR activation. Gene activation in eukaryotes can be achieved by fusing dCas9 to the transcription activation domains of the nuclear factor-kB transactivating subunit (p65) or to VP64 (four repeats of the herpes-simplex VP16 activation domain) 34,35. Multiplex gene activation has even been used for reprogramming primary mouse fibroblasts into induced neuronal cells 36.

Epigenome editing. Fusion of dCas9 to different effector enzymes enables targeted epigenetic modifications, such as acetylation and methylation of histones and methylation of DNA. For example, dCas9-Tet1 was used to demethylate the CGG-expansion mutation in the FMR1 gene and reverse its silencing, which is associated with fragile X syndrome 37.

Genomically Engineered Organoid Models

The marriage of the fast-evolving field of 3D organoid technology with powerful genome engineering technologies has opened the way to a wide range of opportunities in biomedical research, from studying human organ development to exploring new clinical applications. Human embryonic development and disease progression can be best investigated in vitro by using three-dimensional (3D) stem cell differentiation cultures. Many aspects of structural organization and functionality of in vivo organs can be recapitulated by employing organoid models—3D self-organizing structures derived from pluripotent or somatic stem cells 38,39.

Recent technologies are modifying the genomic content in organoid cells in order to faithfully recapitulate disease and development processes, and therefore bring great promise for biomedical research and translational applications. Advances in CRISPR-Cas9 genome engineering technologies now allow investigators to introduce disease-causing mutations for disease modeling but also to generate reporter cell lines for monitoring of specific cell lineages 40,41. Organoids can be grown in petri dishes from human pluripotent stem cells (PSCs) and are increasingly being used as human model systems for disease modeling, drug screening and safety testing, as well as in tissue-replacement therapies.

For disease modeling, for example, isogenic organoid control and disease-specific cultures can be established through genome editing by either introducing mutations in wild-type cells or correcting mutations in patient-derived cell lines 42. Also, therapeutic approaches for various diseases involving drug treatment or genomic editing could be first beta-tested on patient iPSC-derived organoids prior to clinical trials. Reporter cell lines, on the other hand, enable us to develop new types of organoids, and are a must-have tool to identify and isolate specific or rare cell types for biochemical or genomic assays (eg RNA-seq) 41.

Biomaterials in Stem Cell Research and Therapy

The generation of induced pluripotent stem cells (iPSCs) over a decade ago sparked great enthusiasm and promises for developing human disease models, better platforms for drug discovery, and more far-reaching use of autologous cell-based therapy. iPSC research applications and clinical translation have been hampered, however, by limitations associated with safety, efficacy, and scalability of traditional iPSC protocols. Biomaterials are currently being developed to overcome these challenges and allow for improved iPSC derivation, expansion, and differentiation methods 43,44. Implementing biomaterials in traditional iPSC culture platforms also facilitates the investigation of iPSC biology and may allow control over the behavior of iPSCs and their progeny in vitro and in vivo.

Biomaterials—materials selected or designed to interact with biological systems—can advance iPSC research, for example, by controlling the kinetics of reprogramming factors via micro/nanoparticle based systems (eg NanoScript) 45. By creating stem-cell-like niches that incorporate key niche factors, biomaterials may allow precise regulation of stem cell fate and function, and provide far more efficient methods for iPSC expansion and differentiation compared to traditional protocols. For iPSC expansion, 3D culture platforms are advantageous over 2D monolayers by increasing the space between cells and thereby preventing stacking/agglomeration and by providing spatial-temporal signals that are essential for cell proliferation and function, such as 3D cell-ECM interactions and growth factor gradients 46, 47. One biomaterial successfully employed for efficient long-term iPSC expansion is PNIPAAm-PEG 48.

For applications of iPSCs in regenerative medicine, biomaterials are being used to generate tissue-mimetic microenvironments that permit directed cell proliferation, lineage specific differentiation, and formation of functional tissue substitutes in vitro 49. Biomaterials could improve iPSC-based disease models, as the combination could better recapitulate disease pathology and have superior scalability and flexibility for creating personalized models 50,51. Another implementation of biomaterials in iPSC-derived organ systems is 3D bioprinting. Three-dimensional bioprinting of biomaterials, biochemicals, and living cells with precise spacial control can be used to generate 3D functional tissues/organs for transplantation or modeling applications (eg “organ-on-a-chip”) 52,44,53.

Viral Vector Platforms for Gene Therapy

Adeno-associated virus (AAV) vectors are one of the most interesting and impactful biomedical technologies in development. They pose an interesting biological puzzle through their life cycle, adaptability and surprisingly complex interactions with a myriad of host cell factors on the cell surface, in the cytosol and in the nucleus. AAV vectors are highly impactful as they are the leading gene delivery platform spearheading multiple preclinical and clinical successes involving gene replacement, silencing and editing. Recent achievements include the micro-dystrophin gene replacement therapy for Duchenne muscular dystrophy 54, the now commercially approved AAV1 (Glybera, 2)- and AAV2 (Luxturna, 3)-based platforms, but also Cas9 reconstitution by PTS (intein-mediated protein trans-splicing) 55,56.

AAV as a Virus

AAV is found in humans and non-human primates and is not known to cause any human diseases. It has a single-stranded DNA genome of about 4.7 kb enclosed in an icosahedral protein capsid roughly 26 nm in diameter 57. The viral capsid is made up of three types of protein subunits, VP1, VP2 and VP3, repeated to a total of 60 copies in a 1:1:10 ratio, respectively. The viral genome houses three genes and is flanked by two inverted terminal repeats (ITRs) that largely serve as origins of replication and packaging signals. Through alternative splicing and translation from different start codons, multiple proteins can be synthesized from the same viral gene. The rep gene encodes four proteins required for viral replication. The cap gene encodes the three protein subunits that construct the capsid. A third gene is hidden within the cap coding sequence but in a different reading frame, and it encodes the assembly activating protein (AAP) which stimulates virion assembly 58,59. AAV can integrate into the genome of human cells in a locus designated AAVS1 on chromosome 19 to establish latency 60.

AAV as a Vector for Gene Delivery

To achieve their potential as vectors for in vivo gene delivery, recombinant AAV (rAAV) genomes contain therapeutic gene expression cassettes in place of AAV protein-coding sequences. The ITRs are the only viral sequences shared between wild-type and recombinant AAVs, as they are essential for genome replication and packaging during vector production. Removal of the viral coding sequences increases the rAAV packaging capacity, being able to accommodate genomes up to 5 kb, and decreases their immunogenicity and cytotoxicity when delivered in vivo 61.

Many ongoing studies investigate AAV biology, the molecular interactions between the capsid and target cell surface receptors as well as the downstream events following internalization, culminating with second-strand synthesis and eventual integration 62,63,64. Prevalent serotypes (classified based on their cell surface antigens) are presumed to recognize distinct cell receptors and thereby display different cell type and tissue type preferences, or tropism profiles 64. Upon binding to cell surface receptors, rAAV particles are internalized into endosomes where they undergo pH-dependent structural changes and traffic through the cytosol along the cytoskeletal network 65.

After entering the nucleus through the nucleus pore complex, the viral capsid is uncoated to release the genome 66. Identifying the key host factors and underlying mechanisms regulating intracellular trafficking would greatly benefit recombinant AAV (rAAV) transduction efficiency. Before it can be transcribed, the single-stranded rAAV genome must be converted to a double-stranded form. Second-strand synthesis is initiated by the self-primed 3’-ITR, and the assembly of double-stranded genomes can be aided further by delivering virions packaged with complementary rAAV strands 67,68. Replication of the rAAV genome and assembly of virion particles are dependent on host cell factors and other elements provided by helper viruses, such as adenovirus (AdV) and herpes simplex virus (HSV).

Vector Design

We are witnessing a new stage in the development of viral vector-based gene therapies, with technologies such as high-throughput sequencing/screening and advanced computational tools that greatly support capsid directed evolution and in silico approaches 69,70,71,72. Additionally, techniques such as error-prone PCR and capsid shuffling are commonly employed to generate capsid libraries for screening 69-73. Ongoing work in rAAV genome design, on the other hand, is leading with strategies such as micro-gene, dual-vector, or PTS-mediated protein reconstitution that enable the use of larger transgenes 56,54,74,75.

Although gene replacement therapies have enjoyed the most clinical success, gene addition can address some of the most urgent unmet medical needs, such as heart failure and infectious diseases—by supplying growth factors, tuning signaling pathways, etc. Editing strategies, on the other hand, can silence or mutate target genes or introduce new ones at specific genomic loci. These are fascinating avenues to follow, and they bring real hope for effective, safe and cost-efficient gene therapy in the near future.

Challenges

But what are the key challenges for producing clinical-grade vectors? Some of the main questions addressed by ongoing investigations include the following: Are recombinant baculovirus platforms better than stable eukaryotic cell lines for achieving large-scale production? Are ex vivo potency assays using human explants applicable for different targeted tissues? Are they a good replacement for animal models? Particularly intriguing is also the interaction with the host immune system. Would autologous Treg cells co-delivered in vivo with the gene therapy efficiently modulate the rAAV-triggered immune response? These and similar interrogation points are actively investigated to achieve safe and affordable clinical-grade vector production.

In Vivo Imaging for Preclinical Research and Diagnostics

Ultrasound scanning and magnetic resonance imaging (MRI) are in vivo imaging technologies that entered clinical use several decades ago. But other imaging techniques are making their mark in different areas of diagnostics, treatment monitoring and animal models of human disease. Recent achievements based on novel imaging technologies include confirming the reduction in neuroinflammation in depression patients following cognitive therapy, and new insights on disease progression from animal models for infectious, metastatic cancers and heart disease. In vivo imaging offers the unique advantage of visualizing biological processes in real time down at the molecular level. The challenges are therefore to provide sufficient spatial and temporal resolution, and avoid disturbing the processes themselves through the observation procedure.

In vivo imaging technologies can be grouped into five main categories: computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), optical techniques and ultrasound scanning 76. Optical techniques are the youngest among them and are based on two forms of luminescence: fluorescence and bioluminescence imaging. Fluorescence is a physical process whereby proteins or dyes emit light of a certain wavelength after absorbing electromagnetic radiation. Bioluminescence, on the other hand, involves a biochemical process whereby enzymes break down a substrate to generate light. These two techniques have revolutionized in vivo imaging of biological processes in cells and small laboratory animals. It is becoming increasingly possible to combine cellular-level spatial resolution with whole-body imaging to reveal cellular behavior in vivo at the molecular level.

A main clinical breakthrough achieved with bioluminescence imaging is the establishing of animal models for various metastatic cancers. A study from 2018 by Li et al, for example, describes a patient-derived xenograft model to simulate the transition of bladder urothelial cell carcinoma to metastatic cancer 77. Similarly, fluorescence imaging was used to establish a mouse model of cardiac damage resulting from myocardial infarction 78. While clinical use of optical imaging for cardiac diagnostics is only in its early years, it is already being widely applied for rapid and non-invasive recognition of pathogenic bacteria in wounds 79. MolecuLight (Toronto, Canada) is a handheld device that makes use of this imaging technology by emitting a low intensity violet light at a wavelength of 405 nm to excite bacterial fluorescent molecules. Bacterial molecules that can be visualized with fluorescence imaging include porphyrins, which are cyclic organic compounds produced by some bacteria that exhibit fluorescence, and pyoverdines, which are fluorescent siderophores that are required for pathogenesis in many cases 79.

AI and Deep Learning in Diagnostics and Imaging

Over the past 5 years, increased computational power combined with neural networks-based algorithms have advanced AI and machine learning (ML) to the stage of having a significant impact on many fields, including research and healthcare. ML proved to be extremely valuable for diagnostics and research through its two central capabilities: detecting weak signals amidst noise and enhancing low resolution images. One such example venture was undertaken by Freenome, a Silicon Valley company, that combined ML with molecular biology to develop a highly accurate blood-based colorectal cancer test. They use ML to integrate the signals from multiple analytes (eg DNA, RNA and protein) and thereby achieve greater sensitivity and specificity than individual tests on their own. Beyond early cancer diagnostics, ML-powered analysis of liquid biopsies is also useful for monitoring treatment so as to fine-tune the therapy. Researchers at Cambridge Cancer Genomics (CCG) are applying AI to analyze mutations in the circulating cell-free (cf) DNA from patients’ blood samples and thereby follow treatment progression and check for relapse. Further, by matching patients, and their genomic signature, to appropriate clinical trials to find relevant studies, this strategy constitutes an example of personalized medicine build on machine learning.

On the medical imaging front, AI is being increasingly employed to overcome the resolution limit of light-microscopy systems without the need for sophisticated and expensive high-resolution microscopy 80. Although electron microscopy will always offer a higher resolution, optical technologies can visualize living biological processes in real time. The diffraction resulting from light rays bending around the edges of the lens system determines the so-called Abbe limit: the shortest possible distance between two objects that can be distinguished by a viewer. The Abbe limit was reformulated by Richard Feynman on the basis of time, stating that two different objects can be resolved only if the time taken for rays from each of them to reach the eye piece differs by more than one period of the wavelength used (http://www.feynmanlectures.caltech.edu/I_30.html). This paved the way for a number of methods under the emblem of super-resolution microscopy, relying on either the new rule or various other aspects of physics 81. ML technologies, however, are circumventing the need for such sophisticated systems by using the information contained in low-resolution images in order to generate high-resolution ones 82. The point is that ML can be trained by repeatedly predicting high-resolution images from low-resolution ones, comparing predicted with actual high-resolution images, and adjusting the model accordingly. Multiple such iterations can lead to highly accurate predictions of real high-resolution images.

Nucleic Acid Based Nanotechnology

Due to their unique structural and functional properties and ability to fold into precise and complex shapes, DNA and RNA nanostructures are being developed for applications both in biology and electronics. Although both have applications in nanotechnology, DNA has the added advantage that huge molecules can be compacted into a super-dense storage medium that houses a massive amount of information in each cell. The ability of DNA to self-assemble makes it an ideal candidate for building nanostructures with uses both within and outside biology 83,84,85. Non-biological applications take advantage of the optical and electronic properties of DNA nanoparticles and include sensor devices, or components for digital storage or processing 86. Computing devices based on DNA nanowires could be much denser than conventional optical lithography tools, the size of which is limited by the wavelength of the illuminating light.

A promising application of DNA nanotechnology in biology is the construction of high-precision sensors, capable of detecting molecular compounds in disease diagnosis. DNA molecules can be positioned at regular intervals on a DNA origami tile to generate so-called DNA domino circuits 87. Such devices could be employed as high-precision sensors to detect specific combinations of RNA strands indicative of a given disease in patient samples. Enclosing the molecular circuits inside vesicles that are permeable to molecular signals being detected will provide some protection against enzyme degradation. This could ultimately enable molecular signature-directed cell targeting, selectively killing different types of cancer cells, for example. Ongoing studies are evaluating the commercial and clinical viability of DNA self-assembled nanostructures for a wide variety of applications in biology.

Modulating long non-coding RNAs for Functional Studies

Long non-coding RNAs (lncRNAs) have emerged as important regulators of many cellular functions. Our increasing understanding of their biology progressively introduced different methods to modulate lncRNA expression levels for the assessment of biological function. The human genome project revealed that only about 2% of our DNA code for protein, while next-generation sequencing technologies uncovered the fact that a substantial portion of our non-coding genome is transcribed into RNA. We are now aware of tens of thousands of lncRNAs—transcripts over 200 nucleotides in length that do not appear to code for protein—cumulatively spread among all human chromosomes. While certain lncRNAs were found to play key roles in critical cellular processes—such as XIST lncRNA involved in X chromosome inactivation—the vast majority of lncRNAs were not correlated to known cellular functions 88-91.

Methods for efficient and selective lncRNA loss-of-function and gain-of-function are therefore of high value and intensively being pursued. Generally, such methods involve either alterations of the corresponding genomic DNA (eg gene modification of recruitment of transcription regulators) or direct targeting of the RNA transcript (eg RNA knockdown or transfection of RNA molecules). Importantly, while some lncRNA loci function in trans, producing a lncRNA transcript that functions at distant genomic sites, other lncRNA loci regulate gene expression in cis, acting to enhance or repress neighboring genes on the same chromosome. Both the transcription and the splicing of a lncRNA can regulate the expression of a neighboring protein coding gene 91,92. Many genomic lncRNA sequences also overlap with coding genes 93.

Methods targeting lncRNA transcripts

Based on an established method for interfering with protein coding genes, RNAi platforms have been successfully scaled up for pooled genetic screens for lncRNA function 94,95. However, off-target effects introduce a significant risk of false positives in such RNAi-based approaches. ASOs (antisense oligonucleotides) present an alternative to RNAi for degrading lncRNAs. ASOs are 15-20 nucleotide single-stranded DNA oligomers chemically modified to increase knockdown efficiency and decrease in vivo toxicity 96,97. A more recent method to selectively degrade lncRNAs uses Cas13 family of ribonucleases 98,99. As sgRNAs (targeting CRISPR-Cas13 to lncRNAs) can be stably expressed from viral vectors, Cas13-based methods may be well suited for genome-scale functional screening of lncRNA transcripts.

Methods targeting genomic loci

Homologous recombination-directed genetic deletion is an established gene loss-of-function strategy, one that has been improved with the advent of CRISPR-Cas9-based gene editing 100-102. Two mammalian lncRNAs, XIST and H19, were deleted through replacement of the lncRNA locus with a drug selection cassette, demonstrating their roles in dosage compensation and imprinting, respectively 103,104. Knock-in of polyA sites leads to premature transcription termination and is also an effective lncRNA loss-of-function strategy 105.

CRISPR methods for decreasing lncRNA expression

Nuclease-inactive Cas9 (dCas9) fused to transcription activators or repressors proved to be highly effective at modulating the expression of lncRNAs without amending the underlying genomic DNA sequence. In the CRISPRi system, dCas9 fused to the Krab repressor domain silences transcription through steric blockade of RNA polymerase elongation and local deposition of H3K9me3 methylation marks, which attract heterochromatin formation 34,106-108.

Methods for lncRNA gain-of-function

lncRNAs can readily be packaged into viral vectors for cellular transduction. For example, lncRNA-LET and Plscr4 were overexpressed using viral vectors in order to study their function in hypoxia and cardiac hypertrophy, respectively 109,110. Providing exogenous lncRNAs, however, does not preserve information encoded within and adjacent to the genomic lncRNA locus, and therefore cannot be used to study cis-acting mechanisms. But lncRNA overexpression can also be achieved using CRISPR-Cas9 systems to localize transcription activation domains just upstream of the TSS of lncRNAs. Such CRISPR systems successfully employed in eukaryotic cells include dCas9 (nuclease-defective Cas9) fusions to the transcription activation domains of the nuclear factor-kB transactivating subunit (p65) or to VP64 (four repeat of the herpes simplex VP16 activation domain) 34,111,108. CRISPRa systems are increasingly employed, for instance, in genome-wide screens to identify lncRNAs that mediate drug resistance in cancer cells 112,113. As lncRNAs are gaining momentum as key regulators of cellular functions, we also are to improve our toolbox of methods to modulate their expression and test their functions.

Author: Sebastian Florescu, PhD

HOMEPAGE

Published Online: June 2020

References

1. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep 2, 666–673 (2012).
2. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).
3. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).
4. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).
5. Vitak, S. A. et al. Sequencing thousands of single-cell genomes with combinatorial indexing. Nat. Methods 14, 302–308 (2017).
6. Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011).
7. Zhao, J. L. et al. Conversion of danger signals into cytokine signals by hematopoietic stem and progenitor cells for regulation of stress-induced hematopoiesis. Cell Stem Cell 14, 445–459 (2014).
8. Fan, H. C., Fu, G. K. & Fodor, S. P. A. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).
9. Bodenmiller, B. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867 (2012).
10. Francis, J. M. et al. EGFR variant heterogeneity in glioblastoma resolved through single-nucleus sequencing. Cancer Discov 4, 956–971 (2014).
11. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
12. Sottoriva, A. et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl. Acad. Sci. U.S.A. 110, 4009–4014 (2013).
13. Gill, B. J. et al. MRI-localized biopsies reveal subtype-specific differences in molecular and cellular composition at the margins of glioblastoma. Proc. Natl. Acad. Sci. U.S.A. 111, 12550–12555 (2014).
14. Su, W., Gao, X., Jiang, L. & Qin, J. Microfluidic platform towards point-of-care diagnostics in infectious diseases. J Chromatogr A 1377, 13–26 (2015).
15. Butler, T. P. & Gullino, P. M. Quantitation of cell shedding into efferent blood of mammary adenocarcinoma. Cancer Res. 35, 512–516 (1975).
16. Yeo, T. et al. Microfluidic enrichment for the single cell analysis of circulating tumor cells. Sci Rep 6, 22076–12 (2016).
17. Lim, S. B. et al. Addressing cellular heterogeneity in tumor and circulation for refined prognostication. Proc. Natl. Acad. Sci. U.S.A. 116, 17957–17962 (2019).
18. Yu, W. et al. Nrl knockdown by AAV-delivered CRISPR/Cas9 prevents retinal degeneration in mice. Nat Commun 8, 14716–15 (2017).
19. Eyquem, J. et al. Targeting a CAR to the TRAC locus with CRISPR/Cas9 enhances tumour rejection. Nature 543, 113–117 (2017).
20. Ren, J. et al. A versatile system for rapid multiplex genome-edited CAR T cell generation. Oncotarget 8, 17002–17011 (2017).
21. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).
22. Ran, F. A. et al. Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity. Cell 154, 1380–1389 (2013).
23. Zhu, S. et al. Genome-scale deletion screening of human long non-coding RNAs using a paired-guide RNA CRISPR-Cas9 library. Nat. Biotechnol. 34, 1279–1286 (2016).
24. Suzuki, K. et al. In vivo genome editing via CRISPR/Cas9 mediated homology-independent targeted integration. Nature 540, 144–149 (2016).
25. Lackner, D. H. et al. A generic strategy for CRISPR-Cas9-mediated gene tagging. Nat Commun 6, 10237–7 (2015).
26. Schmid-Burgk, J. L., Höning, K., Ebert, T. S. & Hornung, V. CRISPaint allows modular base-specific gene tagging using a ligase-4-dependent mechanism. Nat Commun 7, 12338–12 (2016).
27. Choi, P. S. & Meyerson, M. Targeted genomic rearrangements using CRISPR/Cas technology. Nat Commun 5, 3728–6 (2014).
28. Torres, R. et al. Engineering human tumour-associated chromosomal translocations with the RNA-guided CRISPR-Cas9 system. Nat Commun 5, 3964–8 (2014).
29. Maddalo, D. et al. In vivo engineering of oncogenic chromosomal rearrangements with the CRISPR/Cas9 system. Nature 516, 423–427 (2014).
30. Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).
31. Nishida, K. et al. Targeted nucleotide editing using hybrid prokaryotic and vertebrate adaptive immune systems. Science 353, aaf8729–aaf8729 (2016).
32. Hart, T. et al. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell 163, 1515–1526 (2015).
33. Koike-Yusa, H., Li, Y., Tan, E.-P., Velasco-Herrera, M. D. C. & Yusa, K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2014).
34. Gilbert, L. A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).
35. Perez-Pinera, P. et al. RNA-guided gene activation by CRISPR-Cas9-based transcription factors. Nat. Methods 10, 973–976 (2013).
36. Black, J. B. et al. Targeted Epigenetic Remodeling of Endogenous Loci by CRISPR/Cas9-Based Transcriptional Activators Directly Converts Fibroblasts to Neuronal Cells. Cell Stem Cell 19, 406–414 (2016).
37. Liu, X. S. et al. Rescue of Fragile X Syndrome Neurons by DNA Methylation Editing of the FMR1 Gene. Cell 172, 979–992.e6 (2018).
38. Clevers, H. Modeling Development and Disease with Organoids. Cell 165, 1586–1597 (2016).
39. Sasai, Y. Cytosystems dynamics in self-organization of tissue architecture. Nature 493, 318–326 (2013).
40. Schwank, G. et al. Functional repair of CFTR by CRISPR/Cas9 in intestinal stem cell organoids of cystic fibrosis patients. Cell Stem Cell 13, 653–658 (2013).
41. Liu, X.-P., Koehler, K. R., Mikosz, A. M., Hashino, E. & Holt, J. R. Functional development of mechanosensitive hair cells in stem cell-derived organoids parallels native vestibular hair cells. Nat Commun 7, 11508–11 (2016).
42. Hockemeyer, D. & Jaenisch, R. Induced Pluripotent Stem Cells Meet Genome Editing. Cell Stem Cell 18, 573–586 (2016).
43. Williams, D. F. On the nature of biomaterials. Biomaterials 30, 5897–5909 (2009).
44. Belair, D. G., Le, N. N. & Murphy, W. L. Design of growth factor sequestering biomaterials. Chem. Commun. (Camb.) 50, 15651–15668 (2014).
45. Patel, S. et al. NanoScript: a nanoparticle-based artificial transcription factor for effective gene regulation. ACS Nano 8, 8959–8967 (2014).
46. Sant, S., Hancock, M. J., Donnelly, J. P., Iyer, D. & Khademhosseini, A. BIOMIMETIC GRADIENT HYDROGELS FOR TISSUE ENGINEERING. Can J Chem Eng 88, 899–911 (2010).
47. Han, Y. L. et al. Engineering physical microenvironment for stem cell based regenerative medicine. Drug Discov. Today 19, 763–773 (2014).
48. Lei, Y. & Schaffer, D. V. A fully defined and scalable 3D culture system for human pluripotent stem cell expansion and differentiation. Proc. Natl. Acad. Sci. U.S.A. 110, E5039–48 (2013).
49. Bratt-Leal, A. M., Nguyen, A. H., Hammersmith, K. A., Singh, A. & McDevitt, T. C. A microparticle approach to morphogen delivery within pluripotent stem cell aggregates. Biomaterials 34, 7227–7235 (2013).
50. Rosenzweig, A. Illuminating the potential of pluripotent stem cells. N. Engl. J. Med. 363, 1471–1472 (2010).
51. Takayama, K. et al. 3D spheroid culture of hESC/hiPSC-derived hepatocyte-like cells for drug toxicity testing. Biomaterials 34, 1781–1789 (2013).
52. Levato, R. et al. Biofabrication of tissue constructs by 3D bioprinting of cell-laden microcarriers. Biofabrication 6, 035020 (2014).
53. Neufurth, M. et al. Engineering a morphogenetically active hydrogel for bioprinting of bioartificial tissue derived from human osteoblast-like SaOS-2 cells. Biomaterials 35, 8810–8819 (2014).
54. Duan, D. Systemic AAV Micro-dystrophin Gene Therapy for Duchenne Muscular Dystrophy. Mol. Ther. 26, 2337–2356 (2018).
55. Truong, D.-J. J. et al. Development of an intein-mediated split-Cas9 system for gene therapy. Nucleic Acids Res. 43, 6450–6458 (2015).
56. Chew, W. L. et al. A multifunctional AAV-CRISPR-Cas9 and its host response. Nat. Methods 13, 868–874 (2016).
57. Berns, K. I. & Muzyczka, N. AAV: An Overview of Unanswered Questions. Hum. Gene Ther. 28, 308–313 (2017).
58. Sonntag, F., Schmidt, K. & Kleinschmidt, J. A. A viral assembly factor promotes AAV2 capsid formation in the nucleolus. Proc. Natl. Acad. Sci. U.S.A. 107, 10220–10225 (2010).
59. Sonntag, F. et al. The assembly-activating protein promotes capsid assembly of different adeno-associated virus serotypes. J. Virol. 85, 12686–12697 (2011).
60. Philpott, N. J., Gomos, J., Berns, K. I. & Falck-Pedersen, E. A p5 integration efficiency element mediates Rep-dependent integration into AAVS1 at chromosome 19. Proc. Natl. Acad. Sci. U.S.A. 99, 12381–12385 (2002).
61. Dong, J. Y., Fan, P. D. & Frizzell, R. A. Quantitative analysis of the packaging capacity of recombinant adeno-associated virus. Hum. Gene Ther. 7, 2101–2112 (1996).
62. Huang, L.-Y., Halder, S. & Agbandje-McKenna, M. Parvovirus glycan interactions. Curr Opin Virol 7, 108–118 (2014).
63. Agbandje-McKenna, M. & Kleinschmidt, J. AAV capsid structure and cell interactions. Methods Mol. Biol. 807, 47–92 (2011).
64. Nonnenmacher, M. & Weber, T. Intracellular transport of recombinant adeno-associated virus vectors. Gene Ther. 19, 649–658 (2012).
65. Xiao, P.-J. & Samulski, R. J. Cytoplasmic trafficking, endosomal escape, and perinuclear accumulation of adeno-associated virus type 2 particles are facilitated by microtubule network. J. Virol. 86, 10462–10473 (2012).
66. Nicolson, S. C. & Samulski, R. J. Recombinant adeno-associated virus utilizes host cell nuclear import machinery to enter the nucleus. J. Virol. 88, 4132–4144 (2014).
67. Fisher, K. J. et al. Transduction with recombinant adeno-associated virus for gene therapy is limited by leading-strand synthesis. J. Virol. 70, 520–532 (1996).
68. Ferrari, F. K., Samulski, T., Shenk, T. & Samulski, R. J. Second-strand synthesis is a rate-limiting step for efficient transduction by recombinant adeno-associated virus vectors. J. Virol. 70, 3227–3234 (1996).
69. Paulk, N. K. et al. Bioengineered AAV Capsids with Combined High Human Liver Transduction In Vivo and Unique Humoral Seroreactivity. Mol. Ther. 26, 289–303 (2018).
70. Maheshri, N., Koerber, J. T., Kaspar, B. K. & Schaffer, D. V. Directed evolution of adeno-associated virus yields enhanced gene delivery vectors. Nat. Biotechnol. 24, 198–204 (2006).
71. Kotterman, M. A. & Schaffer, D. V. Engineering adeno-associated viruses for clinical gene therapy. Nat. Rev. Genet. 15, 445–451 (2014).
72. Yang, L. et al. A myocardium tropic adeno-associated virus (AAV) evolved by DNA shuffling and in vivo selection. Proc. Natl. Acad. Sci. U.S.A. 106, 3946–3951 (2009).
73. Lisowski, L. et al. Selection and evaluation of clinically relevant AAV variants in a xenograft liver model. Nature 506, 382–386 (2014).
74. Duan, D., Yue, Y., Yan, Z. & Engelhardt, J. F. A new dual-vector approach to enhance recombinant adeno-associated virus-mediated gene expression through intermolecular cis activation. Nat. Med. 6, 595–598 (2000).
75. McClements, M. E. & MacLaren, R. E. Adeno-associated Virus (AAV) Dual Vector Strategies for Gene Therapy Encoding Large Transgenes. Yale J Biol Med 90, 611–623 (2017).
76. Lauber, D. T. et al. State of the art in vivo imaging techniques for laboratory animals. Lab. Anim. 51, 465–478 (2017).
77. Gills, J. et al. A patient-derived orthotopic xenograft model enabling human high-grade urothelial cell carcinoma of the bladder tumor implantation, growth, angiogenesis, and metastasis. Oncotarget 9, 32718–32729 (2018).
78. Chen, J., Ceholski, D. K., Liang, L., Fish, K. & Hajjar, R. J. Variability in coronary artery anatomy affects consistency of cardiac damage after myocardial infarction in mice. Am. J. Physiol. Heart Circ. Physiol. 313, H275–H282 (2017).
79. Hill, R., Rennie, M. Y. & Douglas, J. Using Bacterial Fluorescence Imaging and Antimicrobial Stewardship to Guide Wound Management Practices: A Case Series. Ostomy Wound Manage 64, 18–28 (2018).
80. Wang, H. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16, 103–110 (2019).
81. Schulz, O. et al. Resolution doubling in fluorescence microscopy with confocal spinning-disk image scanning microscopy. Proc. Natl. Acad. Sci. U.S.A. 110, 21000–21005 (2013).
82. Ouyang, W., Aristov, A., Lelek, M., Hao, X. & Zimmer, C. Deep learning massively accelerates super-resolution localization microscopy. Nat. Biotechnol. 36, 460–468 (2018).
83. Rothemund, P. W. K. Folding DNA to create nanoscale shapes and patterns. Nature 440, 297–302 (2006).
84. Douglas, S. M. et al. Self-assembly of DNA into nanoscale three-dimensional shapes. Nature 459, 414–418 (2009).
85. Geary, C., Rothemund, P. W. K. & Andersen, E. S. RNA nanostructures. A single-stranded architecture for cotranscriptional folding of RNA nanostructures. Science 345, 799–804 (2014).
86. Roller, E.-M. et al. Hot spot-mediated non-dissipative and ultrafast plasmon passage. Nat Phys 13, 761–765 (2017).
87. Zhang, F. et al. Complex wireframe DNA origami nanostructures with multi-arm junction vertices. Nat Nanotechnol 10, 779–784 (2015).
88. Rinn, J. L. & Chang, H. Y. Genome regulation by long noncoding RNAs. Annu. Rev. Biochem. 81, 145–166 (2012).
89. Quinn, J. J. & Chang, H. Y. Unique features of long non-coding RNA biogenesis and function. Nat. Rev. Genet. 17, 47–62 (2016).
90. Lee, J. T. Epigenetic regulation by long noncoding RNAs. Science 338, 1435–1439 (2012).
91. Engreitz, J. M. et al. Local regulation of gene expression by lncRNA promoters, transcription and splicing. Nature 539, 452–455 (2016).
92. Groff, A. F. et al. In Vivo Characterization of Linc-p21 Reveals Functional cis-Regulatory DNA Elements. Cell Rep 16, 2178–2186 (2016).
93. Derrien, T. et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 22, 1775–1789 (2012).
94. Guttman, M. et al. lincRNAs act in the circuitry controlling pluripotency and differentiation. Nature 477, 295–300 (2011).
95. Ramos, A. D. et al. Integration of genome-wide approaches identifies lncRNAs of adult neural stem cells and their progeny in vivo. Cell Stem Cell 12, 616–628 (2013).
96. Kurreck, J., Wyszko, E., Gillen, C. & Erdmann, V. A. Design of antisense oligonucleotides stabilized by locked nucleic acids. Nucleic Acids Res. 30, 1911–1918 (2002).
97. Geary, R. S., Norris, D., Yu, R. & Bennett, C. F. Pharmacokinetics, biodistribution and cell uptake of antisense oligonucleotides. Adv. Drug Deliv. Rev. 87, 46–51 (2015).
98. Shmakov, S. et al. Discovery and Functional Characterization of Diverse Class 2 CRISPR-Cas Systems. Mol. Cell 60, 385–397 (2015).
99. Grünweller, A. et al. Comparison of different antisense strategies in mammalian cells using locked nucleic acids, 2'-O-methyl RNA, phosphorothioates and small interfering RNA. Nucleic Acids Res. 31, 3185–3193 (2003).
100. Doyle, A., McGarry, M. P., Lee, N. A. & Lee, J. J. The construction of transgenic and gene knockout/knockin mouse models of human disease. Transgenic Res. 21, 327–349 (2012).
101. Jinek, M. et al. RNA-programmed genome editing in human cells. Elife 2, e00471 (2013).
102. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).
103. Marahrens, Y., Panning, B., Dausman, J., Strauss, W. & Jaenisch, R. Xist-deficient mice are defective in dosage compensation but not spermatogenesis. Genes Dev. 11, 156–166 (1997).
104. Ripoche, M. A., Kress, C., Poirier, F. & Dandolo, L. Deletion of the H19 transcription unit reveals the existence of a putative imprinting control element. Genes Dev. 11, 1596–1604 (1997).
105. Friedrich, G. & Soriano, P. Promoter traps in embryonic stem cells: a genetic screen to identify and mutate developmental genes in mice. Genes Dev. 5, 1513–1523 (1991).
106. Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).
107. Gilbert, L. A. et al. Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation. Cell 159, 647–661 (2014).
108. Chavez, A. et al. Highly efficient Cas9-mediated transcriptional programming. Nat. Methods 12, 326–328 (2015).
109. Yang, F. et al. Repression of the long noncoding RNA-LET by histone deacetylase 3 contributes to hypoxia-mediated metastasis. Mol. Cell 49, 1083–1096 (2013).
110. Lv, L. et al. The lncRNA Plscr4 Controls Cardiac Hypertrophy by Regulating miR-214. Mol Ther Nucleic Acids 10, 387–397 (2018).
111. Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).
112. Bester, A. C. et al. An Integrated Genome-wide CRISPRa Approach to Functionalize lncRNAs in Drug Resistance. Cell 173, 649–664.e20 (2018).
113. Boettcher, M. et al. Dual gene activation and knockout screen reveals directional dependencies in genetic networks. Nat. Biotechnol. 36, 170–178 (2018).